Methods for cell type specific profiling to identify drug targets

文档序号：1722051 发布日期：2019-12-17 浏览：26次中文

阅读说明：本技术 用于细胞类型特异性谱分析以鉴定药物靶标的方法 (Methods for cell type specific profiling to identify drug targets ) 是由 N·海因茨 X·许于 2018-02-09 设计创作，主要内容包括：本发明提供了谱分析来自单一细胞类型的多个核的基因和蛋白质表达并比较这些谱以确定细胞群、来自不同受试者的样品和表达疾病表型的细胞之间的可变性的方法。(The present invention provides methods for profiling gene and protein expression from multiple nuclei of a single cell type and comparing these profiles to determine variability between cell populations, samples from different subjects, and cells expressing a disease phenotype.)

1. A method of profiling gene expression of a nucleus, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Labeling at least one nucleus from the treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein characteristic of a nucleus of at least one cell type contained in the tissue sample, wherein the nucleic acid transcript or protein is encoded by a gene selected from tables 19-24;

(d) Nucleic acid transcript and/or protein expression data is collected for the labeled nuclei, thereby profiling gene expression for the nuclei.

2. The method of claim 1, wherein the cell type is derived from cerebellar tissue and is selected from the group consisting of: granulosa cells, purkinje cells, glial cells, bergmann glial cells, dopaminergic neurons, basolateral/stellate cells, astrocytes, brainstem motor neurons, oligodendrocytes, superior motor neurons, inferior motor neurons, and the deep nuclei of the cerebellum.

3. The method of any one of claims 1or 2, wherein the protein is a transcription factor located within the core.

4. The method of claim 1, wherein the cell type is derived from basal ganglia tissue and is selected from the group consisting of: striatal substantia nigra Medium Spiny Neuron (MSN), striatal globus pallidus MSN, striatal cholinergic interneurons, subthalamic nucleus, dopaminergic neurons, and nucleus velutipes (BNST) neurons.

5. The method of claim 1, wherein the cell type is derived from thalamic tissue and is selected from the group consisting of: thalamocortical neurons, thalamocortical neurons and thalamotrochondrial nuclear neurons.

6. The method of claim 1, wherein the cell type is derived from cortical tissue and is selected from the group consisting of: cortical striatal neurons, entorhinal cortical 2/3 layer neurons, rapidly stimulating cortical interneurons, and 2/3 layers of pyramidal cells from prefrontal cortical tissue.

7. the method of claim 1, wherein said cell type is a cholinergic projection neuron from the medial reins of the pineal gland.

8. The method of claim 1, wherein the cell type is derived from hippocampal tissue and is selected from the group consisting of: hippocampal horn region 1(CA1), hippocampal horn region 2(CA2), hippocampal horn region 3(CA3), and dentate gyrus cells.

9. the method of claim 1, wherein the cell type is derived from at least one tissue selected from the group consisting of: brain tissue, brain stem tissue, and spinal cord tissue.

10. a method of isolating a granular cell, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by one of genes Itpr, NeuN, Cdh15, Calb2, Rbfox3, Neurod1, and Reln, wherein the nucleic acid transcript or protein is characteristic of the nuclei of granulosa cells; and

11. The method of claim 2, wherein the at least one labeled nucleus does not comprise a nucleic acid transcript encoded by Olig 2.

12. A method of isolating purkinje cells, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Isolating nuclei from the treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein encoded by one of genes Pcp2, Pvalb, cablel 1, and Itpr 1; and

13. A method of isolating purkinje cells, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

14. a method of isolating astrocytes and oligodendrocytes, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

15. A method of isolating astrocytes, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

16. A method of isolating basket cells, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by Sorcs3 and NeuN, wherein the combination of nucleic acid transcripts or proteins encoded by Sorcs3 is unique to the nuclei of basket cells; and

17. A method of isolating dopaminergic neurons, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by FoxA1, Slc6a3, TH, FoxA2, or Drd2, wherein the nucleic acid transcript or protein is characteristic of the nuclei of dopaminergic neurons; and

18. A method of isolating brainstem motor neurons, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by VaChT or ErrB, wherein the nucleic acid transcript or protein is characteristic of the nuclei of brainstem motor neurons; and

19. The method of any one of claims 1-18, wherein the affinity tag is selected from the group consisting of: antibodies, RNA probes, and DNA probes.

20. the method of any one of claims 10 or 12-18, further comprising:

(d) Nucleic acid transcript and/or protein transcript data is collected for the labeled nuclei, thereby profiling gene expression for the nuclei.

21. The method of any one of claims 1-9 or 20, further comprising:

(e) Comparing the gene expression of the nuclei to gene expression in at least one nucleus of a cell type from a different tissue sample to identify variability in gene expression, thereby obtaining a drug target.

22. The method of any one of claims 1-9 or 20, further comprising:

(e) comparing the gene expression of the nucleus to gene expression of at least one nucleus in a different cell type to identify variability in gene expression, thereby obtaining a drug target.

23. The method of claim 19, wherein the affinity tag further comprises a fluorescent label.

24. The method of claim 23, wherein the nuclei are purified using Fluorescence Activated Cell Sorting (FACS).

25. The method of any one of claims 1-24, wherein the tissue sample is derived from a mammal.

26. The method of claim 25, wherein the mammal is selected from the group consisting of: mouse, human, rat, and additional non-human primates.

27. The method of any one of claims 21 or 22, wherein the tissue sample or the different tissue sample is frozen prior to isolating the nuclei.

28. the method of any one of claims 21 or 22, wherein the tissue sample or the different tissue sample is fresh prior to isolating the nuclei.

29. The method of any one of claims 21 or 22, wherein the tissue sample or the different tissue sample is derived from a female.

30. The method of any one of claims 21 or 22, wherein the tissue sample or the different tissue sample is derived from a male.

31. The method of any one of claims 1-18, wherein the core is contiguous with the endoplasmic reticulum.

32. The method of any one of claims 1-18, wherein the protein is a membrane protein.

33. The method of claim 29, wherein the membrane protein is synthesized in the endoplasmic reticulum.

34. The method of claim 33, wherein the protein is located in the endoplasmic reticulum.

35. The method of any one of claims 1-12, wherein the nucleic acid transcript is located in the nucleus.

36. The method of claim 19, wherein the RNA probe specifically binds to a Chromosome Associated Transcript (CAT) or a poly a transcript.

37. The method of claim 19, wherein the DNA probe specifically binds to a transporter gene.

38. The method of claim 19, wherein the affinity tag is more than one antibody, RNA probe, or DNA probe.

39. The method of claim 38, wherein each affinity tag binds a different factor.

40. The method of claims 1-18, wherein the tissue sample is postmortem.

41. The method of any one of claims 21 or 22, wherein the tissue sample or the different tissue sample is derived from a diseased subject.

42. The method of any one of claims 21 or 22, wherein the tissue sample or the different tissue sample is derived from a healthy subject.

43. The method of claim 41, wherein the diseased subject is affected by at least one condition selected from the group consisting of: ataxia, parkinson's disease, alzheimer's disease and Amyotrophic Lateral Sclerosis (ALS) and huntington's disease.

44. The method of claim 43, wherein the cell type is associated with ataxia and is selected from the group consisting of: purkinje cells, granulosa cells, bergeman glial cells, basketry/stellate cells, astrocytes, oligodendrocytes, and deep cerebellar nuclei.

45. The method of claim 43, wherein the cell type is associated with Parkinson's disease and is selected from the group consisting of: substantia nigra and VTA dopaminergic neurons.

46. The method of claim 43, wherein the cell type is associated with Alzheimer's disease and is selected from the group consisting of: entorhinal cortex 2/3 layer, CA1 hippocampus and CA2/3 hippocampus.

47. The method of claim 43, wherein the cell type is associated with ALS and is selected from the group consisting of: brainstem, cortical motor neurons, and spinal cord motor neurons.

48. The method of claim 41, wherein the diseased subject expresses a disease phenotype.

49. The method of claim 41, wherein the diseased subject does not express a disease phenotype.

50. The method of any one of claims 1-9 or 20, further comprising comparing the spectral analysis of a tissue sample derived from a diseased subject to a tissue sample from a healthy subject.

51. The method of any one of claims 1-9 or 20, further comprising comparing spectral analysis of tissue samples derived from at least two different healthy subjects.

52. The method of claim 19, wherein the more than one antibody, RNA probe, or DNA probe bind to the same agent.

53. The method of any one of claims 1-9 or 20, wherein multiple nuclei from a single cell type are subjected to gene expression profiling.

54. The method of any one of claims 1-9 or 20, wherein multiple nuclei from different cell types are subjected to gene expression profiling.

55. The method of claim 1, further comprising:

(e) Variability between disease stages is identified by comparing the profile analysis of tissue samples from diseased subjects expressing a disease phenotype to the profile analysis of tissue samples from diseased subjects not expressing a disease phenotype.

56. The method of claim 21, further comprising:

(e) Identifying variability between different subjects by comparing spectral analyses of more than one tissue sample, wherein each tissue sample is from a different subject.

57. The method of any one of claims 21 or 22, wherein the drug target is encoded by a gene selected from tables 19-24.

58. A method of profiling epigenetic expression of a nucleus, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Isolating nuclei from the treated tissue sample by contacting the treated tissue with an affinity tag that binds at least one factor specific to a cell type, wherein the affinity tag is selected from the group consisting of: antibodies, RNA probes and DNA probes;

(d) Information on histone modifications, transcription factor binding and nuclear DNA modifications is collected to profile epigenetic expression.

59. A method of profiling a nuclear structure of a nucleus, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(d) Information about the chromosome conformation of the labeled nucleus is collected, and the nuclear structure is thereby spectroscopically analyzed.

60. A method of profiling a nuclear structure of a nucleus, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(d) Performing bisulfite sequencing;

(e) collecting the chromosome conformation of the labeled nucleus, thereby profiling the nuclear structure.

61. A method of isolating nuclei from a single cell type, the method comprising:

(a) Treating a tissue sample containing a plurality of cell types to expose nuclei;

(b) Isolating nuclei from the treated tissue sample by contacting the treated tissue sample with more than two affinity tags that are specific for a unique combination of nucleic acid transcripts and/or proteins of the cell type, wherein each affinity tag specifically binds to a nucleic acid transcript comprising a sequence selected from SEQ ID NO. 1-28,815 or the affinity tag specifically binds to a protein comprising an amino acid sequence selected from SEQ ID NO. 28,816-38, 643; and

Technical Field

The present invention relates to profiling gene and protein expression from multiple nuclei of a single cell type to identify drug targets.

Background

The mammalian Central Nervous System (CNS) is a complex organ containing hundreds of different mixed cell types. The ability to genetically target (Gong, et al (2003) Nature 425, 917-925) and molecularly profile specific Cell types (Heiman, et al (2008) Cell 135, 738-748) has begun to provide insight into the basic characteristics of the mammalian brain, which was found in the initial study of Ramon y Cajal one century ago (Ramon y Cajal, S., et al (1899) Textureof the New vous System of Man and the Vertebrates (Wein New York: Springer), each of which anatomically distinct, classically defined Cell types expresses a set of characteristic genes (Dougherty, et al (2010) Nucleic Acids Res 38, 4218-4230 and Doyle, et al (2008) 135, 749-762) and which confer the essential characteristics for specialized Cell function (Kimm, et al, J-2008, et al, Nature, 2014-159, 2014-748), 295-305). Expression of these genes is dependent on the maintenance of a Cell-specific epigenetic state of tissue nuclear function (Kriaucionis, et al (2009) Science 324, 929-. The application of current profiling techniques in mouse models has also led to the realization of changes in gene expression in affected Cell types by environmental influences (Heiman, et al (2008) Cell 135, 738-748; Shrestha, et al (2015) eLife 4, 289), internal physiological causes (Schmidt, et al (2012) Cell 149, 1152-1163), and disease-causing genetic lesions (Fyffe, et al (2008) Neuron 59, 947-958; Ingram, et al (2016) Neuron 89, 1194-1207). Despite the advances made in experimental systems, the fundamental problem of human brain complexity has not been solved. For example, it is not known how many different cell types exist in the human brain, how these cell types vary between individuals or between species, whether the process of brain senescence is the same between cell types, and why mutations in widely expressed genes can have devastating consequences in one or several selected cell types.

Although there is abundant information about the affected circuits in psychiatric and neurodegenerative diseases, the ability to specifically modulate these circuits requires knowledge of the targets found only in the neurons in the circuits.

A method called Translational Ribosome Affinity Purification (TRAP) has previously been developed to enable the identification of all genes expressed in a cell type of interest. TRAP profiling studies have led to the development of several drugs that have entered clinical research. Although TRAP technology has enabled some drug development, the major limitation of TRAP is that target identification was initially performed using transgenic mice. Although mouse and human are similar, there are known differences in CNS circuits of the two species, and there may also be differences in gene expression in orthologous cell types. Furthermore, it is not clear how much gene expression in a particular cell varies from individual subject to individual subject, and whether these differences affect pathogenesis or response to treatment. In TRAP, a BAC vector was used to generate transgenic mouse strains. See Doyle et al, Cell, 135: 749-762(2008) and Heiman et al, Cell, 135: 738-748(2008), which is incorporated by reference herein in its entirety. This method is then called bacTRAP.

in this method, the EGFP-L10a nucleosome fusion protein is targeted to a desired cell type, such as a Purkinje cell. Cell-specific polysome RNA was affinity purified using anti-EGFP coated magnetic beads as described by Heiman et al. Microarrays can be used to analyze purified samples. BACs are typically selected from the gene expression nervous system map (GENSAT) project developed by Nathaniel Heintz, University of Rockfeler (the Rockfee University) (www.gensat.org). In some cases, a transgene may be expressed in more than one cell type. For example, the immunofluorescence of Doyle et al showed that EGFP-L10a was expressed in both Pvalb-positive and NeuN-negative interneurons of the molecular layer of the cerebellum, demonstrating that both stellate and basket cells were targeted. The mRNA was immunoprecipitated and the mRNA samples were analyzed for whole genome transcript profiling. These results were compared to profiling of non-immunoprecipitated samples to identify cell type specific markers.

In order to develop more potent and specific drugs, there is a need to identify candidate targets directly from human and human tissues rather than transgenic mice, and to determine the extent to which these targets vary between individuals. Thus, there is a long felt need for universally applicable methods that enable molecular studies of defined cell types in wild-type animals and humans. Furthermore, non-genetic techniques are needed to complement the discovery of specific characteristics of neural cell types and circuits in model systems and to provide insight into the potentially unique characteristics of the human nervous system.

Disclosure of Invention

Various embodiments of the present invention provide methods of profiling gene expression of a nucleus, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; labeling at least one nucleus in a treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein characteristic of a nucleus of at least one cell type contained in the tissue sample, wherein the nucleic acid transcript or protein is encoded by a gene selected from tables 19-24; purifying the at least one labeled nucleus; and collecting nucleic acid transcript and/or protein expression data for the labeled nuclei, thereby profiling gene expression of said nuclei.

In some embodiments, the cell type is derived from cerebellar tissue and is selected from the group consisting of: granulosa cells, purkinje cells, glial cells, bergmann glial cells, dopaminergic neurons, basketero/stellate cells, astrocytes, brainstem motor neurons, oligodendrocytes, superior motor neurons, inferior motor neurons, and cerebellar deep nuclei (cerebellar deep nuclei). In some embodiments, the protein is a transcription factor located within the nucleus. In some embodiments, the cell type is derived from basal ganglia tissue and is selected from the group consisting of: striatum substantia nigra mesospino neurons (MSNs), striatum pallidum MSNs (striattoppallidal MSNs), striatum cholinergic interneurons (striatal cholinergic interneurons), subthalamic nuclei (subthalamic nuclei), dopaminergic neurons, and cula striata nucleus (BNST) neurons. In some embodiments, the cell type is derived from thalamic tissue and is selected from the group consisting of: thalamocortical neurons, thalamocortical neurons and thalamotrochondrial nuclear neurons. In some embodiments, the cell type is derived from cortical tissue and is selected from the group consisting of cortical striatal neurons, entorhinal cortical 2/3 layer neurons, fast-stimulating cortical interneurons, and 2/3 layers of pyramidal cells from prefrontal cortical tissue. The cell type is a cholinergic projection neuron from the medial reinsertion tissue of the pineal gland. In some embodiments, the cell type is derived from hippocampal tissue and is selected from the group consisting of: hippocampal horn region 1(CA1), hippocampal horn region 2(CA2), hippocampal horn region 3(CA3), and dentate gyrus cells. In some embodiments, the cell type is derived from at least one tissue selected from the group consisting of: brain tissue, brain stem tissue, and spinal cord tissue.

Various embodiments of the present invention provide methods of isolating granulosa cells, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by one of genes Itpr, NeuN, Cdh15, Calb2, Rbfox3, Neurod1, and Reln, wherein the nucleic acid transcript or protein is specific to the nuclei of granulosa cells; and purifying the at least one labeled nucleus. In some embodiments, the at least one labeled nucleus does not comprise a nucleic acid transcript encoded by Olig 2.

Various embodiments of the present invention provide methods of isolating purkinje cells, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein encoded by one of genes Pcp2, Pvalb, cablel 1, and Itpr 1; and purifying the at least one labeled nucleus.

various embodiments of the present invention provide methods of isolating purkinje cells, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein encoded by one of the genes Lypd6, Pvalb, Kit, NeuN, Itpr, and Sorcs 3; and purifying the at least one labeled nucleus.

Various embodiments of the present invention provide methods of isolating astrocytes and oligodendrocytes, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein encoded by one of the genes Olig2, Pdga, Cspg4, Mag, Mbp, and Mog; and purifying the at least one labeled nucleus.

Various embodiments of the present invention provide methods of isolating astrocytes, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with an affinity tag that binds to a nucleic acid transcript or protein encoded by one of the genes Aldhlal, Gfap, S110b, and Slcla 3; and purifying the at least one labeled nucleus.

Various embodiments of the present invention provide methods of isolating basket cells, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by Sorcs3 and NeuN, wherein the combination of nucleic acid transcripts or proteins encoded by Sorcs3 is unique to the nuclei of basket cells; and purifying the at least one labeled nucleus.

Various embodiments of the present invention provide methods of isolating dopaminergic neurons, the methods comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by FoxA1, Slc6a3, TH, FoxA2, or Drd2, wherein the nucleic acid transcript or protein is characteristic of the nuclei of dopaminergic neurons; and purifying the at least one labeled nucleus.

Various embodiments of the present invention provide a method of isolating brainstem motor neurons, the method comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue sample with at least one affinity tag that binds to a nucleic acid transcript or protein encoded by VaChT or EnB, wherein the nucleic acid transcript or protein is characteristic of the nuclei of brainstem motor neurons; and purifying the at least one labeled nucleus.

In some embodiments, the affinity tag is selected from the group consisting of an antibody, an RNA probe, and a DNA probe. In some embodiments, the method further comprises collecting nucleic acid transcript and/or protein transcript data for the labeled nucleus, thereby profiling gene expression of the nucleus. In some embodiments, the method further comprises: comparing the gene expression of the nuclei to gene expression in at least one nucleus of the cell type from different tissue samples to identify variability in gene expression, thereby obtaining a drug target. Alternatively, in some embodiments, the method further comprises comparing the gene expression of the nucleus to gene expression of at least one nucleus in a different cell type to identify variability in gene expression, thereby obtaining a drug target.

In some embodiments, the affinity tag further comprises a fluorescent tag. In some embodiments, the nuclei are purified using Fluorescence Activated Cell Sorting (FACS). In some embodiments, the tissue sample is derived from a mammal. In some embodiments, the mammal is selected from: mouse, human, rat, and other non-human primates. In some embodiments, the tissue sample or the different tissue sample is frozen prior to isolating the nuclei. In some embodiments, the tissue sample or a different tissue sample is fresh prior to isolating the nuclei. In some embodiments, the tissue sample or the different tissue sample is derived from a female. In some embodiments, the tissue sample or the different tissue sample is derived from a male. In some embodiments, the core is contiguous with the endoplasmic reticulum. In some embodiments, the protein is a membrane protein. In some embodiments, the membrane protein is synthesized in the endoplasmic reticulum. In some embodiments, the protein is located in the endoplasmic reticulum. In some embodiments, the nucleic acid transcript is located in the nucleus. In some embodiments, the RNA probe specifically binds to a Chromosome Associated Transcript (CAT) or a poly a transcript. In some embodiments, the DNA probe specifically binds to a transporter gene. In some embodiments, the affinity tag is more than one antibody, RNA probe, or DNA probe. In some embodiments, each affinity tag binds to a different factor. In some embodiments, more than one antibody, RNA probe, or DNA probe binds to the same agent.

In some embodiments, the tissue sample is post-mortem. In some embodiments, the tissue sample or the different tissue sample is derived from a diseased subject. In some embodiments, the tissue sample or the different tissue sample is derived from a healthy subject. In some embodiments, the diseased subject is affected by at least one condition selected from the group consisting of: ataxia, parkinson's disease, alzheimer's disease and Amyotrophic Lateral Sclerosis (ALS), and huntington's disease. In some embodiments, the cell type is associated with ataxia and is selected from the group consisting of: purkinje cells, granulosa cells, bergeman glial cells, basketry/stellate cells, astrocytes, oligodendrocytes, and deep cerebellar nuclei. In some embodiments, the cell type is associated with parkinson's disease and is selected from the group consisting of: substantia nigra and VTA dopaminergic neurons. In some embodiments, the cell type is associated with alzheimer's disease and is selected from the group consisting of: entorhinal cortex 2/3 layer, CA1 hippocampus and CA2/3 hippocampus. In some embodiments, the cell type is associated with ALS and is selected from the group consisting of: brainstem, cortical motor neurons, and spinal cord motor neurons. In some embodiments, the diseased subject expresses a disease phenotype. In some embodiments, the diseased subject does not express a disease phenotype. In some embodiments, the method further comprises comparing the spectral analysis of a tissue sample derived from a diseased subject to a tissue sample from a healthy subject. In some embodiments, the method further comprises comparing the spectral analysis of tissue samples derived from at least two different healthy subjects. In some embodiments, the method further comprises identifying variability between disease stages by comparing the profiling of tissue samples from diseased subjects expressing a disease phenotype to profiling of tissue samples from diseased subjects not expressing a disease phenotype.

In some embodiments, the method further comprises performing gene expression profiling on a plurality of nuclei from a single cell type. In some embodiments, the method further comprises performing gene expression profiling on a plurality of nuclei from different cell types. In some embodiments, the method further comprises identifying variability between different subjects by comparing spectral analyses of more than one tissue sample, wherein each tissue sample is from a different subject. In some embodiments, the drug target is encoded by a gene selected from tables 19-24.

Various embodiments of the present invention provide a method of profiling epigenetic expression of a nucleus, the method comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue with an affinity tag that binds to at least one factor specific to a cell type, wherein the affinity tag is selected from the group consisting of an antibody, an RNA probe, and a DNA probe; purifying the at least one labeled nucleus; and collecting information about histone modifications, transcription factor binding, and nuclear DNA modifications to profile epigenetic expression.

Various embodiments of the present invention provide a method of profiling a core structure of a core, the method comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue with an affinity tag that binds to at least one factor specific to a cell type, wherein the affinity tag is selected from the group consisting of an antibody, an RNA probe, and a DNA probe; purifying the at least one labeled nucleus; and collecting information about the chromosome conformation of the labeled nucleus, and analyzing the nuclear structure from the spectrum.

Various embodiments of the present invention provide a method of profiling a core structure of a core, the method comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; isolating nuclei from the treated tissue sample by contacting the treated tissue with an affinity tag that binds to at least one factor specific to a cell type, wherein the affinity tag is an antibody; isolating gDNA from the nucleus; performing bisulfite sequencing; and collecting the chromosome conformation of the labeled nucleus, thereby profiling the nuclear structure.

Various embodiments of the present invention provide a method of isolating nuclei from a single cell type, the method comprising: treating a tissue sample containing a plurality of cell types to expose nuclei; separating nuclei from a treated tissue sample by contacting the treated tissue sample with more than two affinity tags that are specific for a unique combination of nucleic acid transcripts and/or proteins of the cell type, wherein each affinity tag binds to a nucleic acid molecule comprising a sequence selected from the group consisting of seq id NOs: 1-28,815 or the affinity tag binds specifically to a nucleic acid transcript comprising a sequence selected from SEQ id nos: 28,816-38, 643; and purifying the at least one labeled nucleus, thereby isolating nuclei from the cell type.

Drawings

FIG. 1 shows an embodiment of a sorting method that can be used in the cell profiling techniques described herein. "A" represents the nucleus of the cell type of interest and "B" represents the nucleus of another cell type.

Fig. 2A shows the results of flow cytometry sorting of 3 cell types from the cerebellum using Itpr1 and NeuN. The combination of Itpr1 and NeuN was used to isolate 3 populations: purkinje cells, granular cells, and glia. Itpr1 is located on the x-axis and NeuN is located on the y-axis. The Itpr1+ population is the nucleus of Purkinje cells and the NeuN + population is the nucleus of glial cells. The NeuN +/Itpr 1-core (largest group) represents granulosa cells. Figure 2B shows gene expression profiles from RNAseq data comparing sorting of purkinje nuclear RNA from transgenic animals (GFP sorting) with Itpr1+ nuclei. Pcp4 is a purkinje marker (enriched in sorted purkinje nuclei compared to cerebellar unsorted nuclei), while Calb2 is a granular cell marker (enriched in unsorted nuclei compared to purkinje).

Figure 3A shows the flow cytometry sorting results for nuclear staining and sorting of dopaminergic neuron nuclei. Figure 3B compares RNAseq traces from unsorted mesencephalic nuclei and sorted dopaminergic nuclei.

Figure 4 provides a genome browser view showing three traces of mouse, rat and human-nuclear RNA levels from granulosa cells, cytoplasmic RNA levels from total cerebellum, and ATACseq DNA accessibility from total cerebellum-to reveal species-and cell type-specific differences.

Fig. 5A and 5B show the results of flow cytometry sorting of nuclei from two different human subjects.

Fig. 6 shows the flow cytometry sorting results, which estimate the size of nuclei by a combination of forward scattered light (FSC) and back scattered light (SSC).

Fig. 7A, 7B, 7C, and 7D show flow cytometry results for 4 different staining strategies for human cerebellar nuclei.

Fig. 8 provides a boxplot showing paired Pearson correlation coefficients within cell types and between cell types of different individuals.

Fig. 9 provides a dot plot showing the paired Pearson correlation coefficients for each sample disrupted by cell type and species.

Fig. 10 is a graph showing the change in FosB expression in granulosa cells, basketcytes, and glial cells over an autolysis time of 5 to about 25 hours.

Fig. 11A, 11B, and 11C provide scatter plots showing that three cell type-specific senescence down-regulated gene expression across age of the genes. Fig. 11A shows the results of Aqp 7. FIG. 11B shows the results of Asic1, and FIG. 11C shows the results of Robo 2.

FIG. 12 provides stress responses and immediate early gene expression between three cell types in three individuals, among the Fos + and Fos-individuals. Gene traces show pooled expression from three Fos + and three age and gender matched Fos-individuals.

Fig. 13A shows gene expression of the fos gene and immediate early genes in granulosa cells, astrocytes and glial cells. Fig. 13B shows gene expression of long term stress response genes in these cells. Fig. 13C shows the expression of stress-related genes, such as activated astrocyte markers GFAP, S100B, Exportin 1(Xpo1, also known as leucine-rich nuclear export signal (Nes)), and Musashi RNA binding protein 1(Msi1) in glial cells, separated from markers of longer-term stress.

FIG. 14 shows RNAseq data for cell type specific Chromosome Associated Transcripts (CAT).

Detailed Description

I. Introduction to the word

Complex tissues are composed of many cell types. The molecular characterization of different cell types in a single tissue has previously been defined using the Translation Ribosome Affinity Purification (TRAP) method. The TRAP method requires the use of transgenic animals, which creates a number of challenges. For example, some disease models are difficult to reproduce in animals, and animal models do not necessarily summarize all aspects of human disease, such as the effects on human neurons. In addition, aging studies require maintenance of specific transgenic lines for many years. In addition, the TRAP method requires accurate profiling of fresh samples containing high quality RNA.

The determination of molecular properties of genetically targeted cell types has led to fundamental insight into mouse brain function and dysfunction. Embodiments of the cell profiling techniques described herein provide strategies for exploring gene expression events in specific cell types in a wide range of species for the intensive and reproducible study of gene expression to identify markers for each cell type or disorder. Results for mouse, rat, and human neurons and glia are shown herein, and reveal features of homologous human cell types. By expressing hundreds of orthologous cell-specific genes, it was shown that the classically defined homologous neuron and glial cell types differ between rodents and humans. Evidence for differential activity of these genes was obtained using a combination of epigenetic mapping, RNAseq, quantitative PCR and immunofluorescence mapping. Studies of 16 human postmortem brains showed a cell-specific molecular response to aging. It was observed that the response of human neurons and glia to aging was cell type specific, and analysis of post-mortem human brain tissue also revealed induction of a collective strong response to unknown external events experienced by three of the 16 donors. The compositions and methods herein establish a comprehensive approach for analyzing unique molecular events associated with specific pathways and cell types in a variety of human disorders.

Recent advances in human genome sequencing and analysis technology have resulted in increased knowledge of the complex genetic causes of human psychiatric and neurological diseases (Burguire, et al (2015) Curr Opin Neurobiol 30, 59-65; Hinz, F.I., et al (2017) Molecular Genetics of neurologic degradation. PubMed-NCBI. Cold spring Harb Perspectrum Biol 9, a 023705; Vorstman, et al (2017) Nat Rev Genet 18, 362-376, each of which is incorporated herein by reference in its entirety). In some cases, reconstructing the causative mutations in the mouse genome resulted in an experimental model that was sufficiently accurate for studying the molecular basis of the disorder (Lombardi, et al (2015) j.clin. invest.125, 2914-. In other cases, informative animal models remain elusive (Lavin, M.F. (2013). DNA Repair12, 612-.

Techniques that have attempted whole genome profiling of specific cell types in human tissues, including laser capture microdissection and single cell sequencing techniques, were used prior to the development of the cell profiling techniques described herein. Neither technique produces a reliable deep profile of the identified cell type. Laser capture microdissection requires specialized equipment and training, and the results are very dependent on the precise dissection method and tissue anatomy; therefore, there is a great difference in results between samples.

The data from single cell RNAseq was also highly variable from cell to cell. Single cell RNAseq is also problematic in the nervous system because it is difficult to separate neurons from each other. In studies to isolate neurons, only cell bodies were captured, while neuronal processes (axons and dendrites) were lost. It is well known in the art that processes in neurons are very large compared to the size of the cell body, and also have their own local translation mechanism. Therefore, gene expression analysis of only the cell body is insufficient for genes expressed in neuronal processes, some of the latter being the most important genes for determining neuronal identity. This bias can be avoided by profiling gene expression in the nucleus (which synthesizes all the RNA of the cell). The depth of data from any single cell is not sufficient to establish a comprehensive profile, so many in the field of cell profiling do not accept this approach.

Human disease is a problem that is experienced throughout the cell population. The cell types from single cell profiling must be defined post hoc and the profiling may be obscured by disease states. Furthermore, the sequencing results of single cell RNAseq are not deep enough to detect subtle changes that may occur in the disease. For these reasons, cell types should be considered as populations. Doing so using the single cell method requires sequencing thousands of individual cells, and some cell types are very rare, and therefore require enrichment to study diseases that affect only these rare cell types. Without enrichment, there is little discrimination between technical and biological variability.

To be able to identify human specific targets, the cell profiling techniques described herein have been developed for molecular profiling of specific cell types of post-mortem human tissue. The cell profiling techniques described herein have broad applications, including studying gene expression and epigenetic changes to identify target drugs or other therapeutic modalities.

Because cells in the CNS have complex morphology and cannot be efficiently separated from each other, the cell profiling techniques described herein classify nuclei rather than whole cells. In some embodiments of the method, after isolating nuclei from the brain region of interest, the nuclei may be immunostained with an antibody specific for a transcription factor or transcript that specifically labels the cell type of interest. Immunofluorescence determines whether the core can be labeled with antibodies against membrane proteins. Staining was observed in the Endoplasmic Reticulum (ER) around the nucleus. Alternatively, RNA probes or DNA probes may be used to label the cell type of interest.

In some embodiments, isolated nucleic acids can be used for gene expression profiling after purification of the marker population using Fluorescence Activated Cell Sorting (FACS). FIG. 1 provides an embodiment of a sorting method that can be used in the cell profiling techniques described herein. "A" represents the nucleus of the cell type of interest and "B" represents the nucleus from another cell type.

In certain embodiments, algorithms can be used to identify cell-specific and enriched gene expression for a single cell type. For example, one analytical method for identifying and quantifying cell-specific and enriched mRNA in multiple cell populations is known as Specificity Index (SI), e.g., Dougherty et al, Nucleic Acids Research, 38 (13): 4218-4230, which is incorporated herein by reference in its entirety. Gene expression is measured in a variety of cell types. These values were first normalized in replicates and then globally normalized across cell types. A negative control was used to filter out non-specific background. The filtered values are iteratively compared to other unfiltered samples in the data set and the ratio of each set of samples is calculated. These sets are ranked from highest to lowest to prevent extreme outliers from deviating from subsequent analysis and to make the analysis more robust to data sets that are difficult to normalize. These levels are averaged to provide SI. The displacement test was used to assign a p-value to each SI-value to provide a list of significant clustered genes in the cell type of interest. The lower the SI value, the greater the specificity of the gene for the cell type.

The cell profiling techniques described herein have a range of applications, including: identifying human targets for treatment, understanding how human-to-human variability leads to variable penetrance of treatment, studying early changes that occur in human degenerative diseases, identifying drug targets, and developing treatment methods based on comparative analysis of vulnerable and spare cell populations. The cell profiling techniques described herein can be used to profile any cell type of interest. For example, gene expression of different cell types can be compared to identify genes uniquely expressed in the cell type of interest to identify putative drug targets. By comparing heterogeneity between different patients, biomarkers of disease may also be revealed, which may be used to identify patients before symptoms appear, or to select a subset of patients for which therapeutic intervention will be more effective.

In other embodiments, gene expression is compared between species (e.g., mouse and human) using the cell profiling techniques described herein to identify common and species-specific genes. This information also informs of drug discovery, as mouse specific targets are commercially less valuable. Furthermore, species consensus targets allow basic studies in mice to be more likely transformed into humans. Finally, human specific targets may be responsible for the development of certain diseases in humans, but it is difficult to develop the same disease in mice. Follow-up with common targets may provide new drug targets that cannot be identified only in mice.

Another application of the cell profiling techniques described herein is to compare gene expression between the same cell types in the same tissue in different individuals to identify inter-individual variability. Genome-wide association studies (GWAS) are currently used to study inter-individual variability at the DNA level, but these techniques do not show whether DNA variability affects gene expression and function. The data generated by the cell profiling techniques described herein can be used for patient stratification and combined with GWAS data to determine why variability exists across individuals in response to treatment.

In contrast to the TRAP method, the cell profiling techniques described herein do not require the use of transgenic animals. The cell profiling techniques described herein can be used for post-mortem tissues from any species, including non-model organisms, such as humans and other primates.

in certain embodiments of the invention, a tissue sample is treated and an antibody specific for a protein expressed in a cell type of interest is added to the sample. The fluorescent label of the antibody is then added to the sample. Nuclei from the cell type of interest are isolated from the tissue sample by fluorescence. For example, FACS is used to separate nuclei based on fluorescence intensity. Sorting can be performed using antibodies specific for transcription factors located in the nucleus (e.g., Matevosan et al, J.Vis. exp. (13), e717, doi: 10.3791/717, (2008), which is incorporated herein by reference in its entirety) or using antibodies specific for proteins located in the Endoplasmic Reticulum (ER). Since many neurons have been identified based on membrane markers, antibodies specific for membrane proteins synthesized in the ER can be used to study the nervous system. In addition, it was observed that part of the endoplasmic reticulum remained attached to the isolated nuclei.

Antibodies used to label and sort nuclei from specific cell types can be selected from previous studies of the TRAP method to identify factors specifically expressed in the cell type of interest. Instead of antibodies, RNA or DNA probes specific for nucleic acid transcripts located in the nucleus or ER of the cell type of interest are used. For example, Mo et al (Neuron, 86: 1364-1384(2015), which is incorporated herein by reference in its entirety) analyzed excitatory, PV interneurons, and VIP neurons in transgenic mice using Camk2a, PV, and VIP as targets, respectively. In Mo, about 50% of the cortical nuclei are Camk2a positive, about 3% PV positive, and about 2% VIP positive.

The methods herein provide for the study of cellular vulnerability in human neurodegenerative diseases, as well as circuit-based molecular characterization of cell types involved in obsessive-compulsive disorder (obsessive complex disorder), drug addiction, autism spectrum disorders (autism spectrum disorder), and age-dependent cognitive loss. In some embodiments, the objective of the cell profiling techniques described herein is to obtain human CNS cell type data to establish characterization of brain circuits or pathways involved in important human neurological and neurodegenerative diseases. For example, cell-specific data from mice is obtained using antibodies specific for proteins identified from fully processed poly a + RNA enriched for nuclei and Chromosome Associated Transcripts (CAT). RNA/DNA probes to these can be designed for additional labeling and sorting. Specifically, purified GFP + nuclei and isolated RNA from transgenic animals developed for the TRAP method were analyzed using PolyDT beads to capture poly a + RNA as mature RNA. Enrichment of RNA in ER adjacent to nuclear membrane was observed. These data provide details on transcripts enriched in ER near the nucleus and these are putative targets for antibodies and RNA/DNA probes that can be used for the sorting step in the cell profiling techniques described herein. The methods herein result in the generation of highly reproducible cell type specific data and confirmation of cell specific expression profiles by in situ hybridization or immunofluorescence of newly discovered cell specific markers in human tissue samples. This data provides confirmation that transcript profiles from a large number of tissues (e.g., from the brain) can be used to identify new cell-specific probes.

Gene nomenclature

the Gene symbols used herein with the ENSEMBL Gene ID refer to genes from mice, rats and humans. Unless otherwise indicated, the gene names corresponding to each gene symbol are shown in table 1.

TABLE 1 Gene symbols and names

For mice, tables 2 and 2b provide unique enssembl identifiers corresponding to the mouse genes (MUSG), transcripts (MUST) and proteins (if available) (MUSP) analyzed in the experiments herein. The unique identifier of each enssembl entry has been modified to remove the first five leading zeros (0) of the identifier following the ensusg, ensusst and ensmsps tags. GenBank transcripts (nucleic acids or proteins) are included if there is no ENSEMBL transcript or protein identifier.

TABLE 2 ENSEMBL ID of mice

Table 2b contains other genes in mice. The table identifies unique ENSEMBL identifiers corresponding to the mouse genes (MUSG), transcripts (MUST) and proteins (if available) (MUSP) that were profiled in the experiments herein. The unique identifier of each enssembl entry has been modified to remove the first five leading zeros (0) of the identifier following the ensusg, ensusst and ensmsps tags. GenBank transcripts (nucleic acids or proteins) are included if there is no ENSEMBL transcript or protein identifier.

TABLE 2b ENSEMBL and GenBank ID of mice

For rats, table 3 provides unique ENSEMBL identifiers corresponding to the rat genes (MUSG), transcripts (MUST) and proteins (if available) (MUSP) analyzed in the experiments herein. The unique identifier of each enssembl entry has been modified to remove the first five leading zeros (0) of the identifier following the ensusg, ensusst and ensmsps tags.

TABLE 3 ENSEMBL ID of rats

For human samples, table 4 provides unique ensumbl identifiers corresponding to the human genes (ENSG), transcripts (ENST) and proteins (if available) (ENSP) analyzed in the experiments herein. The unique identifier of each ENSEMBL entry has been modified to remove the first five leading zeros (0) of the identifier following the ENSG, ENST, and ENSP tags.

Table 4: human ENSEMBL ID

Methods for the isolation and profiling of nuclei from cell types

Many of the problems of human biology can be effectively studied in defined cell populations. For example, due to the genetic complexity of these disorders (Burguire, E., et al (2015) Curr Opin Neurobiol 30, 59-65.; Hinz, et al (2017) Cold Spring Harb Perspectrum Biol 9, a 023705; Vorstman, et al (2017) Nat Rev Genet 18, 362-376, each incorporated herein by reference in its entirety), the probabilistic Nature of neuronal cell loss (Clarke, et al (2000). Nature 406, 195-199 and Clarke, et al (2005) Brain Research Bulletin 65, 59-67, each incorporated herein by reference in its entirety), and the variable impact of aging on disease onset and progression (Corrada, et al (2010) Ann. neuron.67, 114. Nature 121 and Niccoli, et al (2012) Curry 22, R741-R752, each incorporated herein by reference in its entirety), an understanding of the molecular events associated with human neurodegenerative events can be obtained by longitudinal studies of a large number of cell types from human samples. In view of these factors, the methods herein were developed for accurately determining the molecular properties of defined brain cell types, and are robust enough to use post-mortem tissue from the common brain tissue bank without the need for transgenic animals.

The cell profiling techniques described herein were developed as methods for cell type specific analysis that utilize the unique components of nuclear localization and nuclear-related proteins within each cell type to enable robust and reproducible studies of cell type determination in the mammalian brain. The cell profiling techniques described herein allow cell-type specific gene expression profiling of mouse, rat, and human postmortem brains, and these methods can be extended to any organ or tissue sample.

Comparative studies of data generated by The herein described cell profiling techniques from rodent and human brain cell types revealed evolutionary divergence of gene expression profiles from The classically defined highly conserved Cerebellar cell types (D' Angelo, E. (2013) Handbook of The Cerebellum and Cerebellar Disorders, (Dordrecht: Springer Netherlands), p. 765 791; Eccles, J.C. (1967) The Cerebelluma neural Machine (Berlin/Heidelberg/New York: Springer); Llinas, R.R. (1969) Neurobiology of Cerebellar Evaporation and Development (Chicago: American society); each of which is incorporated herein by reference in its entirety). The data also suggest that the molecular mechanism of brain aging proceeds differently in each human cell type. Finally, the data show that robust molecular phenotypes indicative of shared external events can be identified in control postmortem human brains. Relevant studies on the expression and epigenetic studies of human genetic and clinical data using the cytometric analysis techniques described herein will provide insight into other but unrecognized molecular characteristics of human brain function and dysfunction.

transgenic mice expressing the EGFP-L10a ribosomal protein fusion protein under the control of a Cell-type specific driver were previously developed in Doyle et al (2008) Cell 135, 749-762 and Heiman et al (2008) Cell 135, 738-748, each of which is incorporated herein by reference in its entirety. Using these five transgenic animal lines developed for the TRAP method, EGFP + nuclei were purified from specific Cell types either by gene expression using the TRAP method or by using epigenetic characteristics after fluorescence-activated Cell sorting (FACS) (kriucentis, et al (2009) Science 324, 929-. Previous studies have determined that the nuclear RNA profile is also specific for each cell type and has information about cell function (Deal, et al (2011) Nat Protoc 6, 56-68; Henry et al, Nucleic Acids res.2012 Oct; 40 (19): 9691-77704; Mo, et al (2015) Neuron 86, 1369-1384; Steiner, et al (2012) Genome Res 22, 766-777; each of which is incorporated herein by reference in its entirety).

A large limitation of the previously developed cell-type specific molecular profiling, relative to the cell profiling techniques described herein, is that it requires the use of transgenic animals to genetically target the cell type of interest. To overcome this limitation, cell type specific expression of nuclear and endoplasmic reticulum proteins was studied to purify and characterize nuclei from specific cell types in a wide range of species, including human post-mortem brain samples.

In some embodiments of the invention, nuclei are prepared from wild-type or transgenic tissue, fixed using formaldehyde, stained with fluorescent antibodies specific for a given cell type in a brain region of interest, sorted by flow cytometry (e.g., FAC), and then analyzed using RNAseq to find species, cell type, or environment-specific expression profiles.

The cell profiling techniques described herein can be applied to nuclei from any cell type or tissue associated with a particular disease, disorder or condition. For example, nuclei derived from basal ganglia tissue include: striatal substantia nigra Medium Spiny Neurons (MSNs), striatal globus pallidus MSNs (caudate nucleus and nucleus accumbens), striatal cholinergic interneurons, subthalamic nucleus, dopaminergic neurons, and terminalia striatum nucleus (BNST) neurons. For example, nuclei of cell types derived from thalamic tissue include: thalamocortical neurons, thalamocortical neurons and thalamotrochondrial nuclear neurons. For example, nuclei of cell types derived from cortical tissue include: entorhinal 2/3 layer neurons, rapidly stimulating cortical interneurons, and 2/3 layers of pyramidal cells from prefrontal cortical tissue. For example, nuclei of cell types derived from hippocampal tissue include: hippocampal horn region 1(CA1), hippocampal horn region 2(CA2), hippocampal horn region 3(CA3), and dentate gyrus cells.

Cell types can also be associated with the following tissues: medial reins (cholinergic projection neurons to the nucleus of the spine), brain stem (upper motor neurons), spinal cord (lower motor neurons), or all tissues (various types of interneurons, astrocytes and oligodendrocytes). Cell types in the human brain may also be associated with neurodegenerative diseases, such as ataxia, parkinson's disease, alzheimer's disease and Amyotrophic Lateral Sclerosis (ALS). For example, profiling analyses delineated purkinje cells, granulosa cells, bergmann glia cells, baskethro/stellate cells and deep cerebellar nuclei associated with ataxia. The Ventral Tegmental Area (VTA) dopaminergic neurons of the substantia nigra associated with parkinson's disease, or the entorhinal 2/3 layer, CA1 hippocampus and CA2/3 hippocampal neurons associated with alzheimer's disease can also be spectroscopically analyzed. Alternatively, brainstem and spinal motor neurons associated with ALS are profiled.

A. Sorting cell types using nuclear transcription sets

Nuclei can be sorted using antibodies, RNA probes or DNA probes specific for Endoplasmic Reticulum (ER) proteins, membrane proteins synthesized in the ER, or nuclear transcripts. To enrich for ER transcripts translated near the nuclear membrane, poly A pull-down of nuclear transcripts can be performed. In some embodiments, nuclei derived from the cell type of interest are isolated from tissues of transgenic mice that have previously been profiled using the TRAP method to compare the cellular signature results from each technique. The results of profiling of transgenic animals developed for the TRAP method are based on the fact that GFP-tagged ribosomal subunits are assembled in the nucleoli. Alternatively, the tissue from which the nuclei to be sorted are derived is extracted from a wild-type mammal, such as a human, non-human primate, mouse or rat.

Fig. 1 provides an embodiment of a sorting method, also known as NETSseq, that can be used in the cell profiling techniques described herein^TMtechnique or INSPECTION^TM. In FIG. 1, "A" represents the cell type of interest and "B" represents the nucleus of the cell type of interest in FIG. 1.

In some embodiments, strategies for isolating 3 cell types from the cerebellum in mice include antibody binding using Itpr1 (inositol 1, 4, 5-triphosphate receptor type 1; intracellular receptors of the Endoplasmic Reticulum (ER)) as a target for labeling Purkinje cells, NeuN (a gene encoded by an RNA-binding Fox protein family member; NeuN is a neuronal antigen commonly used as a neuronal biomarker) as a target for labeling granulocytes, and Gfap (glial fibrillary acidic protein; one of the major intermediate filament proteins used to distinguish mature astrocytes of astrocytes from glial cells) as a target for labeling glial cells to confirm the presence of these types of cells in the sample. The result combination of Itpr1 and NeuN can then be used to isolate all 3 populations. Alternatively, the nuclei of granulosa cells from wild type mice can be sorted to be Itpr (Itpr +) and NeuN (NeuN +) positive and Olig2(Olig2-) and Sorcs3(Sorcs3-) negative. Nuclei from purkinje cells can be sorted into Itpr1+ and NeuN +. Nuclei from basket cells can be identified as Sorcs3+ and intermediate NeuN-. Nuclei from astrocytes were identified as Sorcs 3-and NeuN-

alternatively, neuronal differentiation 1or Neurod1(Neurod1+) may be used to sort granulosa cells; purkinje cell protein 2or Pcp2(Pcp2+) can be used to sort purkinje cells; septin 4or Sept4(Sept4+) can be used for sorting Burgemann glial cells; collagen beta (1-O) galactosyltransferase 2or Glt25d2, also known as Colgalt2, (Glt25d2+) for classifying cortical pontine pyramidal cells; neurotensin receptor 1or Ntsr1(Ntsr +) is used to classify corticotalamic pyramidal cells. In some embodiments, the following nuclear transcripts may be used to identify the nucleus of each cell type: α -2-macroglobulin (A2m) for Sept4 positive cells (bergmann glia), Pde1a and Colgalt2 (corticobasal cone) for Colgalt2 positive cells, Pde1a and heparan sulfate-glucosamine 3-sulfotransferase 4(Hs3st4) for stimulation of positive cells (excitatory neurons), Pde1a and Hs3st4 for Ntsr1 positive cells (corticobasal cone), Aristaless-related homeobox (Arx) and parvalbumin (pvalb) for PV positive cells (parvalin interneuron), Vasoactive Intestinal Peptide (VIP) for VIP positive cells (VIP interneuron), solute carrier family member 9a 3(c 9a3) for Pcp2 positive cells (purkinje), and groin 4642 positive particles (Grin 73784) for neud positive cells (neugecko).

In some embodiments, nuclei from mouse dopaminergic neurons are sorted using forkhead cassette a1(FoxA1) and dopamine transporter solute carrier family 6 member 3(Dat, also known as Slc6a 3). Dopaminergic neurons were observed to be positive for both FoxA1 and Dat. In some embodiments, other markers of dopaminergic neurons that can be used for sorting include: tyrosine Hydrolase (TH), forkhead cassette a2(FoxA2), and dopamine receptor 2(Drd 2).

In some embodiments, down-regulated genes in sorted nuclear populations may also be used as evidence of the identity of the cell type of the nucleus. For example, dopamine receptor D1(Drd1) and glutamate decarboxylase (Gad) are known not to be expressed in dopaminergic neurons and therefore should be down-regulated in sorted nuclear populations from dopaminergic neurons.

In some embodiments, Wolframin ER transmembrane glycoprotein (Wfs1) and B-cell CLL/lymphoma 11B (Ctip2, also known as Bcl11B) can be used to sort cortical cell types 2/3 layers of pyramidal neurons and 5 th and 6 th layers of pyramidal neurons, respectively, from mice. In some embodiments, ETS variant 1(Etv1, also known as ER81) can be used to sort the nuclei of layer 5 pyramidal neurons. It has been previously observed that Etv1 labels cortical striatal neurons, cortical pontine neurons and potentially other neurons in layer 5 of the cortex. In some embodiments, wfs1 may be used as a marker for carbonic anhydrase 1(CA1) positive neurons, and purkinje cell protein 4(Pcp4) may be used as a marker for carbonic anhydrase 1(CA2) positive dentate gyrus.

In some embodiments, solute carrier family 18 member a3(VaChT, also known as Slc18a3), cartilage lectin (Chodl), and estrogen-related receptor β (EsrrB) can be used as markers for nuclei from mouse brainstem motor neurons. Affinity tags specific for VaChT and ErrB specifically label motor neurons, and ChodI labels all but these neurons. The Chodl-specific antibodies may be replaced with antibodies that directly label brainstem motor neurons.

In some embodiments, the rat nuclei are sorted using at least one membrane protein or nuclear-associated transcript. For example, nuclei from granulosa cells can be identified as Itpr negative (Itpr-), Olig2 negative (Olig2-), Sorcs negative (Sorcs-), and NeuN positive (NeuN +). Nuclei from purkinje cells can be identified as Itpr positive (Itpr +) and NeuN +. Nuclei from basket cells can be identified as Itpr + and moderate NeuN negative (med NeuN-). Nuclei from astrocytes can be identified as Sorcs3 positive (Sorcs3+) and medNeuN-. Nuclei from mature oligodendrocytes can be identified as Sorcs 3-and NeuN-. Nuclei from combinations of mature oligodendrocytes and OPCs can be identified as Olig2 positive (Olig2+) and high NeuN negative (high NeuN-).

In some embodiments, nuclei from purkinje cells derived from human samples can be identified as Itpr + and Id2 positive (Id2+) or FoxP4 positive (FoxP4+) and Itpr1 +. In some embodiments, nuclei from granulosa cells derived from a human sample are identified as FoxP4+ and NcuN +. In some embodiments, nuclei from mature oligodendrocytes and OPCs can be distinguished by the expression level of Olig 2. For example, low expression of Olig2 (Olig2+ low) identified the nucleus as a mature oligodendrocyte and high expression of Olig2 (Olig2+ high) identified the nucleus as an OPC.

The nuclear sorting strategy described herein can be applied to any species.

B. Profiling cell types in sorted nuclei of different species

The genes expressed in each cell type define their function, their response to internal and external signals, and their evolution across species. The cell profiling techniques described herein have proven useful for highly accurate cell type specific gene expression profiling of neurons and glia from mouse, rat and human brain.

In Mo et al (2015) Neuron 86, 1369-1384, which is incorporated herein by reference in its entirety, nuclei from transgenic mice expressing a labeled nuclear membrane protein by the Cre-loxP system were analyzed using RNAseq profiles. Comparison of RNAseq profiling of nuclei isolated from cell types from transgenic and wild type animals from different sources provides evidence that nuclear RNA profiling can be used to determine cell identity and measure the correlation of different cell types. In some embodiments, the transgenic animals may express enhanced GFP (EGFP/L10a fusion protein) in the target cell type. In some embodiments, hierarchical clustering of nuclear RNAseq results from Mo et al sorting and characterization and using the cell profiling techniques described herein provides grouping by cell type, such as interneurons (e.g., VIP interneurons and parvalbumin interneurons) versus pyramidal cells (e.g., cortical cones, subcortical thalamus and irritability).

Even classically defined homogeneous cerebellar cell types are observed to differ from species to species by the expression of hundreds of orthologous genes, and continuous expression of genes expressed by human granulosa cells is often observed in other regions of the mouse brain. Indeed, nuclear RNA profiles are sensitive enough to distinguish different neuronal subtypes, such as mature oligodendrocytes (also known as corticobacter pyramidal neurons) and oligodendrocyte progenitor cells or OPCs (also known as corticobacter pyramidal neurons).

1. Differences in RNA profiles in sorted nuclei from mice

In some embodiments, gene expression is analyzed by RNAseq to generate RNA profiles. In some embodiments, the results are normalized to the average expression of all samples for each gene. For example, the log2 fold change in RNAseq results for nuclei purified by FACS of granulocytes, purkinje cells, bergmann glia cells, oligodendrocytes and OPC can be compared to the normalized RNAseq results for the 250 most variable genes in nuclei of all cell types. In mice, the most variable gene may be selected from M11Rik, M20Rik, H12Rik, O07Rik, O14Rik, A H06Rik, A2, A I17Rik, Aass, Acsm, Actbl, Adra2, Afp, Aqp, Arhgap, Arx, Atp13a, 1a, 2a, B E15Rik, B3gnt, Barhl, Bcl11, Btbd, Cacng, Camk1, Camkv, Caspl, Casq, Cbln, Cblcn, Ckbr, Cd, Cdc42ep, Cdh, Chrm, sys, Clic, Cnppy, Cob, Colla, Col4a, Col6a, Collar, Corpin, Coro, Cp, zyp, zym, Egr3, Egr 817, Cgm, Cll, Cdmct 3, Cmax, Cdmct 3, Edma, Egr3, Edfra, Effr, Cgm, Cdm 3, Cgm, Cmd, Cdm 3, Cgm, Cmax, Cgm 3, Cgm, Cmax, Cdl, Cmax, Cdl, Cgm 3, Cgm, Cdl, Cgm 3, Cdl, Cgm 3, Cdl, Cgm, Cdl, Cgm, Cdl, C, Iltifb, Irx, Itih, Itprip, Kank, Kcnc, Kcnf, Kcnj, Kcnq, Kcnt, Kcnv, Kctd12, Kdm5, Lama, Lamp, Lcat, Ldb, Lfng, Lgr, Lhx, Lhxlos, Lhx, Linc-md, Lrrc, Lyzl, Mall, March, Megf, Meis, Mertk, Metrn, Mgst, Miat, Msl, Mmp, Mppod, Msx, Mybpc, Myh, Slo, Nid, Nkx-2, Npr, NPylr, Nrgn, Nrk, Pddr, Nprc 62, Stp 2, Opprp, Irx, Ith, Itx, Ith, Tph 2, Tph, P, Tph, zfp831, Zic1, Zic2, Zic3, Zic4, and Zic5, or other agents identified herein.

In mice, genes whose expression can be used as markers for identifying granulosa cells or their nuclei include genes with the greatest change in expression compared to normalized expression in unsorted nuclei, including: b16Rik, E18Rik, M08Rik, H02Rik, J17Rik, Ablin, Ablim, Ackr, Adamts, Adcy, Airn, Ak, Als, 1a, 2B, Barh, Bcl2l, Boc, Brinp, Bsn, Btg, C K03Rik, Cacnalc, Cacnale, Cacnna 2d, Cadm, Cadps, Calb, Caln, Camk, Ickk, Car, Cbln, Ccbn, Ccctdc 120, Cd300, Cdh, Celf, Cerk, Cerb, Gmn, Chrd, Cksr, Ccnnm, Cntnp, Cntnap, Dilx, Crtahr, Dakb 2, Dekk, Grd, Gmbn, Gmbh, Gankr, Gackp, Gackd 7, Gackd, Gockd, Gordh, Gordk, Akk, Kackp, Gordp, Gackp, Gackh, Gackp, Gackd, Akk, Kackd, Kackp, Kackd 2, Kackd, C7, C3, mir670, Mmp24, Mpp4, Msra, Ndrg3, Ndst3, Nedd 43, Negr 3, Neto 3, Neurl 13, Neurod 3, Nhsl 3, Nrep, Nrip3, Ntn3, Nxn, Olfm3, Pagr 13, Panx 3, Pclo, Pcssk 3, Ppde 103, Pde 33, Pgm2l 3, Pkib, Pfcch 3, Pclsl Slsl 3, Pcxd 3, pg3672, Ppargc 13, Ppfia 3, Pqlc3, Prkskt 3, Ptss 3, Prchd 3, Pchtfel 3, Sbtcl Slsl 3, Sbtp 3, Sbtc 3, Sbtcd 3, Sbtc 3, Sbtp 3, Sbtc 3, Sshc 3, Sbtc 3, Sktp.

In mice, genes whose expression can be used as markers for identifying purkinje or its nuclei include genes with the greatest change in expression compared to normalized expression in unsorted nuclei, including: n15Rik, O03Rik, L16Rik, H12Rik, E02Rik, O14Rik, A F15Rik, Abhd, Adam, Adamts, Adgrl, Aff, Ank, Ankrd33, Ankrd, Anks1, Apip, Arap, Arhgap, Arhgef, Arnt, 2a, 2B, 6apll, 6v1B, Atrnl, Auts, B3gnt, Baiap, Bcl11, Bean, Brinpt, Bzrap, Cacna1, Cacna2D, Calb, Camk2, Carc, Ccc, Ccqq, Ccc 107, dc85, Cdk, Cds, Cfp 126, Cep, Clec2, Clstn, Cattn, Gtnap 5, Gmm 5, Cg, Ccd 7, Ccd, Cg, Cmd, Faglpp, Faglpr, Faglh, Fhd, F1, Fhd, F1, C, F1, C, F1, C, Fprh, kalrn, Kcnab, Kcnh, Kcnip, Kcnma, Kctd, Kit, Ksr, L3mbtl, Large, Ldhd, Lhx1, Lhx, Linc-md, Lpcat, Lrfn, Lrp, Lrrfip, Lsmem, Magoh, Mcemp, Mdfi, Mir 124-1 hg, Mir138-1, Mir3470, Mtss, Myb, Myo, Nefh, Nefm, Nek, Nell, Ninl, Nkiras, Npr, Nr2f, Nrk, Nsg, Nup, Ocad, Orai, Paxbp, PtPdp, Pde2, Pde5, Stpie 9, Stchh 3, Kpnb, Kprprprprprprprprprprprprc, Tph 1, Sprprprprprprprprprprprprprprprprbcp, Tpfd, Tprpfr 2, Tprpffer, Tph, Pprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprbcp, T12, Tpf 4, Tpf 2, Tprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprprpr, Zfand4, Zfp385a, Zfp385c and Znhit 1.

In mice, genes whose expression can be used as markers for identifying basket cells or their nuclei include genes with the greatest change in expression compared to normalized expression in unsorted nuclei, including: f13Rik, N15Rik, P05Rik, A N18Rik, Acot, Adams, Adarb, Adgrl, Adrb, Agl, AI504432, Aldh18a, Aldh112, Aldob, Ankrd13, Ar, Arl4, Arrdc, Asgr, Ashc, As, Asns, Atplb, 2B, Bex, Bhlhe, Btbd, Clqtnf, Cabp, Cacna1, Cacna2d, Cacng, Camk1, Camk2N, Cars, Cdc136, Ccc 74, Cd59, Cdkl, Cebpb, Cep, Cgan, Chac, Chcd 3, Chst, Chmpp, Cmpp, Gantp, Cntp, Cgmp, Cgmh 3, Cgmfrpr, Cmsk 2, Galkurl, Cmsk, Cgmh 3, Cmsk, Cmsd, Cmsk 2, Cmsk, Lsmpcll, Ly6e, Lypd e, Maged e, Man2a e, March e, Mgat 4e, Mgat e, Mir193 e, Mir384, Mkx, Mpp e, Msantd e, Mtfp e, Mthfd e, Myol e, Myrip, Mytl e, Napll e, Nars, Ndn e, Neurod e, Nlnn, Nmt e, Nos 1e, Nrxn e, Ntrtn e, Osbpl e, Palm, Parml, Penk, Pckp e, Pgrmc e, Ctr e, Slctp e, Slspc e, Slprspc e, Spcram e, Tspc e, Tspc e, Tspc e, Tspc e, Tspc e, Tspc e, Tspc e, Tspc e, Tsp.

In mice, genes whose expression can be used as markers for identifying astrocytes or their nuclei include genes with the greatest change in expression compared to normalized expression in unsorted nuclei, including: o22Rik, G03Rik, J24Rik, P03Rik, A2, A J07Rik, Abi3, Acotll, Acsbg, Actr3, Adra1, AI464131, Akt, Aldh111, Alms-ps, Apoe, Aqp, Arrb, 13a, 1b, AU022751, Ax, Baalc, Bcr, Btbd, Cables, Cacnb, Cacng, Cank, Camk2, Ckin, Ccbs, Cdc, Cd, Cdc42ep, Cdh, Cdrh, Celrr, Clcn, Clu, Cml, Cmay, Cpne, Cxctn, Cyp2D, Cyp4F, D23 Rik, Dadao, Ridk, III, Ridge, III, G2, Gerdh, Edfra, Effr, E, Metrn, Mfge, Micalcl, Mir3093, Mir6375, Mir6390, Mlc, Mho, Msi, Msx, Mt, Mxra, Mybpc, N4bp, Nat, Nbl, Ndrg, Nhsl, Nkain, Nrld, Ntsr, Nudt, Cplah, Paqr, Pax, Pbxip, Pde8, Pdlim, 21, Phka, Phkg, Phyhd, Pitpnc, Pitpnm, Pla2g, Slce, Plekho, Ppp, Plscr, Pltp, Plnb, Pnky, Pnp1a, Por, Prex, Proca, Ptdh, Prr, Tracch, Pamph, Pxpmp, Pglpb, Pglpr, Slash, Slm, Sbpr, Sbpb, Sbpr 7, Rbpr, Sbpr, Sbpb, Sbpr, Sbpb, Sbpr, Tpr1, Tpr, Sbpr.

In mice, genes whose expression can be used as markers for identifying oligodendrocytes or their nuclei include genes with the greatest change in expression compared to normalized expression in unsorted nuclei, including: l24Rik, D05Rik, G06Rik, G19Rik, I06Rik, G07Rik, A17Rik, N05Rik, O12Rik, A M10Rik, A N07Rik, Aatk, Abca8, Adamtsl, Adap, Adipor, Agpat, Ankub, Ano, Apod, Arc, Arhgap, Arhgef, 4, Arrdc, Arsgg, Aspa, Asphd, B3galt, Bche, Bcl2L, Bicd, Bin, Bpgm, C H02Rik, C F03Rik, Car, Carns, Cmdc 152, Ccp110, Cdh, Cdk, Cep, Cers, Chdh, Cldpn, Ptp, gtn, Cyya, Endcp, Gmkap, GmJ, Gmbd 2, Gmbd, Gmbp, Gmbd 2, Gmbp, Gmbd, Gmbp, C3, Cdmdp, Cdk2, Cdmdp, C3, Cdk 7, Cdk, C3, C7, Cdk, C7, C1, C7, C1, C7, C1, C7, C III, C7, C1, C7, C III, C III, C III, Lipaf, Litaf, Lrrc8, Lzts, Mag, Mall, Map6d, Map, Mast, Mboat, Mbp, Mobp, Myold, Myrf, Ndrg, Neat, 213, Nhlrc, Nipal, Nkain, Nmral, Olfml, Opalin, Osbp, Pacrg, Pacs, Pak, Pde4, Pde8, Pex, Phlpp, Piga, Pigz, Pik3c2, Pim, Pip4k2, 2l, Pla2gl, Plcl, Plekhg, Plekhhh, Ppp, Pps, Ppxnb, Pppplbp 4, Prlck, Prix, Prpr, Prmr, Prtma, Ttrbth, Slm, Tprprtf, Slprprprc, Tprrf, Tprrg, Tprrf, Tprm, Tprspb, Tprm, Tpr.

In mice, genes expressing markers useful as markers for identifying Oligodendrocyte Progenitor Cells (OPCs) or their nuclei include genes with the greatest change in expression compared to normalized expression in unsorted nuclei, including: l19Rik, L15Rik, C18Rik, K07Rik, O07Rik, A H06Rik, A C20Rik, Abcg, Abhd, Abtb, Adam, Adm, Alcam, Amz, Arhgap, Asap, Ascl, B3gat, B9d, Basp, Bcast, Bmp, Brinp, Clql, Cacng, Calcl, Cavl, Cav, Cnd, Cd200, Cdh, Cdo, Cfap, Chad, Chst, Cob, Col11a, Colpg 27a, Csg 1nact, nk2a, Csp, Cstn, Csg, Cstxntn, Cysfbn, Dbpht, Dcc, Dpm, Dscsam, Ebmam, Ebfm, Ebg, Nprm, Miffr, Miffm, Miffr, Miffm, Miffh, Miffr, Miffh, Miffr, Miffm, Miffr, Miffm, Miffh, Miffm, Miff, Pdgfra, Pdzd, Pfn, Phlda, Pmepa, Ppfibp, Ppp2r2, Prr, Prss, Ptgfrn, Ptpro, Pxdc, Qpct, Rapgef, Rasgef1, Rcn, Rep, Rgcc, Rin, Rlbp, Rnd, Rnf122, Rpl, Rprm, S100a, S1pr, Sapcd, Sce, Scrg, Sema3, Sema5, Serpline, Sertm, Sh3bp, Sh3rf, Slc1a, Slc22a, Slc35f, Slc44a, Slitrk, Slitrrk, Snhg, Snx, Sox, Sosx, Sptrx, Surf, Suglrd, Suglfa, Tfw, Tszem 3bp, Tszem 3b, Tszem, Rbd, Rbpf, Rbf, Tszem, Tsz 3b, Tszem, Tsz 3, Ttrk, Ttrx, T.

In some embodiments, a set, subset of one, two, three, or more of the listed markers, including 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, or more of the markers, can be used to identify nuclei of any one or more of granulocytes, basket cells, astrocytes, oligodendrocytes, and OPCs of any one of a human, mouse, rat, or non-human primate. Hierarchical clustering can be used to visualize the degree of similarity between RNAseq spectra.

2. Differences in RNA profiles in sorted nuclei from rats

In some embodiments, nuclei from rat tissue are sorted into nuclear groups from granulocytes, purkinje cells, basketcytes, astrocytes, oligodendrocytes, and OPCs. Genes that are differentially expressed compared to the normalized expression in granulosa cells, purkinje cells, astrocytes, oligodendrocytes, and OPCs may be used as markers. For example, the expression or lack of expression of at least one differentially expressed gene is selected from: AABR, Abi, Aqp, Arhgef, 4, B3gnt, Bgn, C1ql, CaCng, Cadps, Calb, Car, Cav, Cbln, Cnd, Cdh, Cdk, Cep, Cldn, Clmp, Col12a, Col5a, Cspg, CyP2d, Enpp, Ermn, Fam107, Fam89, Fat, Gabra, Gabrd, Gdf, Gldc, Gli, Gpr, Grm, hapln2, Hpcal4, Id4, Il16, Itga 16, Itih 16, Itpr 16, Kcnh 16, Kcnj16, Lama 16, Lfng, Lgi 16, Lims 16, Lypd 16, Mag, Mal, March 16, Mobp, Mog, Msx 16, Mt 216, nkin 16, Opalin, Pdgfra, Plch 16, Plk 16, Plxdc 16, Pme 16, ppsff 16, Ppfibp 16, Ppplr 16, Prr5 16, Prrt 16, Qdpr, Rasgrp 16, rbx 16, RGD 1567, RGD 1569, rcb 36184255, slra 72, slra 16, slrc 72, slrc 16, slcm 16, sspc 16, slnc 16, slcm 16, tsrc 16, tsfccl 16, tsrc 16, and a 361553672.

In rats, expression of genes that can be used as markers to identify granulosa cells or their nuclei includes expression of Fat2 and Rbfox3 that are significantly enriched compared to normalized expression in unsorted nuclei. Genes that can be used as markers for identifying purkinje cells or their nuclei include significantly enriched expression of Car8 and Calb 1. Genes that can be used as markers for identifying bergeman glia or its nuclei include the expression of significantly enriched Aldh1L1 and Slc1a 3. Genes that can be used as markers for identifying oligodendrocytes (cortical pontine pyramidal cells) or their nuclei include the expression of significantly enriched Colgalt 2. Genes that can be used as markers for identifying bergmann glia or their nuclei include the expression of a significant enrichment of Hs3st4, which was observed to be corticotalamic cone cell specific, while Pde1a and Csmd1 were observed to be expressed in oligodendrocytes and OPCs (two corticocconic cell types).

In some embodiments, expression of Itih3 may be used as a marker for the bergmann glial cell (an astrocyte) and the non-bergmann glial astrocyte of the cerebellum. Other markers for nuclei of 6 cell types may include: tescalecin (tesc) for granulosa nuclei, carbonic anhydrase 8(Car8) for purkinje nuclei, March11 for basketle nuclei, inter-alpha-trypsin inhibitor heavy chain 3(Itih3) for astrocytic nuclei, Mog for oligodendrocyte nuclei, and platelet-derived growth factor receptor alpha (Pdgfra) for oligodendrocyte progenitor nuclei.

In some embodiments, the Allen Mouse Brain Atlas in situ hybridization database is used to analyze the localization of markers to confirm cell types.

3. differences in RNA profiles in sorted nuclei from humans

Analysis of samples from 16 human postmortem brains revealed that the specific molecular consequences of senescence, sex, autolysis time of tissue samples and stress differ between cell types, although in each case the expression of genes involved in synapse development and maintenance was attenuated. The robust cell type specific molecular pathway indicates that a pathophysiological response occurs in3 out of 16 donors. It was observed that cerebellar granule cells of three brain samples showed strong induction of 224 genes containing many immediate early genes and were enriched for GO classes (protein folding/refolding, apoptosis, transcriptional response to stress, response to external stimuli, atpase activity, etc.) indicative of acute response to some external influences; however, these donors have no apparent shared clinical complaints. As described in the examples, there was clearly no correlation with autolysis time and no induction of glial markers indicative of stroke or brain injury. Although activity-dependent gene expression changes have been extensively characterized in cultured mouse granulosa cells, the responses identified in these three human samples overlap but are unique. The guidance results herein for experimental studies of model systems or iPS cells are aimed at identifying the signals that elicit these responses in the human brain. Molecular profiling using the cell profiling techniques described herein can be applied to any species and provides a means for studying molecular events associated with human cell type function and dysfunction.

In some embodiments, increased expression of Rbfox3 and Fat2 may be used as markers for nuclei from human granulosa cells. Alternatively, increased expression of Marchl1 can be used as a marker for the nucleus of human basket/stellate cells. Alternatively, increased expression of Aldhla1 and Slcla3 can be used as markers for human nuclear astrocytes. Alternatively, increased expression of Mog can be used as a marker for mature oligodendrocytes. Alternatively, increased expression of Pdgfra and chondroitin sulfate proteoglycan 4(Cspg4) can be used as a marker for OPC.

C. Direct definition of human cell types

The cell profiling techniques described herein can also be used independently of the cell types that have been characterized in mice, as there are differences between neurons from mice and humans, and there may be human-specific subsets of neurons. Putative cell-type specific genes for sorting were identified by examining the transcript profile of unsorted nuclei from the region of interest. In mice, it was observed that Chromosome Associated Transcripts (CAT) and poly a + transcripts, which are expressed at low levels in unsorted nuclei, are usually expressed at high levels in one or more cell types. Species-to-species differences were explored for different cell types and these findings were supported by in situ hybridization and immunofluorescence. In the context of the cell profiling methods described herein, the same strategy is used to profile human nuclear RNA from a particular cell type.

Comparative analysis of nuclear spectra

Described herein are isolated and transcriptionally profiled nuclei from various cell types from mice, rats, and humans. For example, the cell type is derived from basal ganglia, thalamus, cerebellum, or hippocampal tissue. Cell types derived from the cerebellum include: purkinje cells, granulosa cells, bergeman glial cells, basketry/stellate cells, astrocytes, oligodendrocytes, and cerebellar deep nuclei cells. Sequencing analysis was performed on these samples to determine inter-individual and inter-species differences. In some embodiments, the results are verified by in situ hybridization on tissue sections.

The driving force for the development of accurate and efficient non-genetic methods for characterizing the molecular properties of defined CNS cell types is the ability to directly study human biology. Large differences were observed between gene expression in the same cell type between mice and humans. In view of the extensive research to determine the genetic cause of human disease and the surprising findings in both animal models and humans: these may include lesions that involve simple changes in gene dosage, so it is necessary to explore the details of cell types in the normal and affected brain. Thus, comparative analysis of data obtained in experimental systems using the cell profiling techniques described herein that exhibit human cell type-specific characteristics has facilitated a reduced understanding of a variety of human diseases affecting CNS circuits.

Recent evolution models (Arendt, D. (2008). Nat Rev Genet 9, 868-. According to this model, the specific characteristics of the homologous cell type may vary as long as it remains defined by the unique common regulatory device. For example, purkinje cell, granulosa cell, or astrocyte gene expression profiles may vary between species, or even within a single cell of a type, without losing its cell type characteristics. This definition accommodates both functional changes in cell types between species and changes in gene expression within cell types due to mutations in cis-regulatory sequences. The data herein record the major differences in orthologous gene expression in each cell type between rodent and human brain. Hundreds of these differences are cell type specific, and in granular neurons, most of these genes are still expressed in other regions of the brain. These data indicate that the fine-tuned biochemistry of homologous human and mouse CNS cell types differ significantly.

The results presented here contribute to two main insights into the evolution of the mammalian brain. First, although they are consistent with examples of cis-regulatory sequence differences previously published as evolutionary mechanisms (Maricic, et al (2012) molecular biology and Evolution 30, 844-852; Prud' homme, et al (2006) Nature 440, 1050-minus 1053; Weyer et al, Mol Biol Evol.2016 Feb; 33 (2): 316-minus 322, 2015; each of which is incorporated herein by reference in its entirety), the lack of highly enriched GO classes for most of the cell-specific events recorded herein strongly suggests that regulatory changes are the major drivers of phenotypic variation between homologous cell types in the mammalian brain. This is consistent with the evolutionary model set forth above, as it allows for substantial evolution of cell functional properties while maintaining the core regulatory complex (CoRC) that defines a given cell type (Aredt, et al (2016) Nat Rev Genet 17, 744-757, which is incorporated herein by reference in its entirety). Second, the expression differences recorded herein are large and the analysis is limited to orthologous genes of high confidence. This data complements previous studies on progenitor cells that reported substantial changes in human gene expression lacking mouse orthologs (Florio et al, science.2015 Mar 27; 347 (6229): 1465-70, which is incorporated herein by reference in its entirety). Taken together, these data sets predict that differences in expression between mouse and human cell types are extensive, cell type specific and phenotypically important.

In certain embodiments where human samples are profiled using the methods herein, the correlation coefficient is between samples (r ═ 0.98 to 1.0), indicating that the results are extremely reproducible and that autolysis time has little effect on nuclear RNA expression profiling. Preliminary evidence for the different effects of gender and age on gene expression in CNS cell types suggests that the cell profiling techniques described herein are sufficiently accurate for detailed studies of sexual binary human behavior and aging of specific brain circuits. For example, comparative analysis of the human brain by the cytometric analysis techniques described herein provides insight into the effects of aging on cell types that are selectively susceptible to late-onset human disease.

A. Determining cell type-specific function that differs between different species

although the organization of cells in the cerebellum is similar between mice and humans, there are many differences. For example, purkinje cells are more dispersed in human cells and NeuN-cells spread in the purkinje layer than in mouse cells, which demonstrates that the human cerebellum has a different tissue than the mouse. Furthermore, purkinje cell bodies and nuclei are larger in size in humans compared to mice. In some embodiments, RNAseq can be used to compare gene expression between species to identify species-specific markers. For example, protein tyrosine kinase 2 β (Ptk2b), solute carrier family 43 member 2(Slc43a2), the glutamate ionophore receptor kainate type subunit 3(Grik3), Itpr1, procalcitonin 9(Pcdh9), and RAS guanine releasing protein 1(Rasgrp1) from the nucleus of granulocytes, astrocytes, and glial cells can be used to distinguish human from mouse nuclei. Since the cerebellum is one of the most conserved brain structures between species, more structural and molecular differences are more easily observed in other brain regions.

Although heterogeneity may be shown between samples from the same species, the genes most specific to each cell type in different species may be used as unique markers for the cell type. In some embodiments, expression of more than one gene may be used to identify a particular cell type. Genes that are differentially expressed compared to the normalized expression of all cell types (e.g., granulosa cells, purkinje cells, astrocytes, oligodendrocytes, and OPCs) can be eliminated from the analysis to identify at least one marker for the cell type of interest.

In some embodiments, the most specific genes are calculated using an algorithm for Specificity Index (SI) as described herein. For example, the most specific genes (ordered by decreasing specificity) in the nuclei of granulosa cells derived from human samples are Cadps, Reln, Synpr, Galnt, Ccm, Srrm, Tmem266, Cckbr, Cerkl, Chn, Grik, Mal, Fat, Adamts, Lcn, Fstl, Vsn, Rit, Slc8a, Slcl7a, Zp, Tspan, Fgf, Prag, Slc6a, Rims, Cdh, Llcam, Mical, 2, Kcnh, Arhgap, Cdk, Pde3, Itga, Tmem, Chgb, Col13a, Rnf152, Kcnk, Il, Als, Barhl, Scn2, bln, Dusp, st, Lurap, Kcnj, Snai, Neckr, Cdhr, Cdhk, Cdhm, Cdhk, Cdhm, Cdhk, Cdhr, Cdhk, Cdhm, Cdhk, Cdhr, Cdhm, Cdhb, Cdhm, Cdfb, Cdhm, Cdfb, Cdf. For example, the most specific genes (ordered by decreasing specificity) in the nuclei of granulosa cells derived from mouse samples are Gabra, Grin2, Fat, Cadps, Ptpn, Chn, Neurod, Calb, Trhde, Il, Ppfia, Cbln, Sidt, Tmem266, Gabrd, Reln, Kcnj, Gprc5, Rims, Tlll, Cbln, Olfn, Slc17a, Chgb, Kcnh, Cdh, Barh, Dusp, Kcnc, Camk, Des, Cerk, Svep, Uncx, Slc8a, Cacna2d, Pxylp, Ptchd, Sel113, Rtn4, Cacnale, Pattj, Atp2b, Marveld, Kcpnk, Rab, Rapg, Rief, Tmem, Tm, Sc 7, St, Sc, Sclip, Tab, Talip, Tab, Talip, Tab. For example, the most specific genes (sorted by decreasing specificity) in nuclei of the granular cells derived from rat samples are Tese, Itga7, Rbfox3, AABR07013140.1, Calb2, Kcnh1, Cdh15, AABR07030880.1, Prrt2, Gabrd, Il16, Scara3, Cbln1, AABR07026032.2, Cadps2, Sv2b, Fat2, Gabra6, Car4, Kcnj12, Nrep, AABR 12, Rtn4 12, Shisa 12, Sel1l 12, Grb 12, Rgl 12, Dusp 12, ccn 12, la3672, Pagr 12, celmip 12, Gprc5 12, Shf, wdr36dr3672, tmdrm 363672, tm36ksm 36kstfr 12, clnbr 12, clrdl 12, calmr 12, caocl 12, caoclmr 12, caoclm3672, caocl 12, caoclm3672, caoclmr 12, cs3672, csnrnbr 12, cs3672, cs36ksn 12, cs36ks3672, cs36ksn 36ksn 36ks3672, cs36ks36ks3672, cs36ksn 12, cs36ks36ks3672, cs3672, cs36ks36ks36ksn 36ks36ks3672, cs3672, cs36ks36ks36ks36ks36ks36ks36ks3672, cs36ks36ksn 12, cs36ks3672, cs36ks36ks3672, cs3672, cs36ksn 36ks36ks36ks36ks36ksn 12, cs36ksn 12, cs3672, cs36ks36ks36ks36ks36ksn 12, cs3672, cs36ks3672, cs36ks36ks36ks36ks36ks36ks36ksn 36ksn 36ks36ks36ks36ks36ks36ks36ks36ks36ks3672, cs3672, cs36ksn 12, cs3672, cs36ks36ks36ks36ks36ksn 12, cs3672.

For example, the most specific genes (sorted by decreasing specificity) in nuclei of purkinje cells derived from mouse samples are Car8, Calb1, Arhgef33, Atp2a3, Itpr1, Ppplr17, Slcla6, Slc9a3, Trpc3, Casq2, Clic6, Plekhd1, Ryrl, Cacnalg, Pcp2, Sycp1, Pde 51, Tspan1, Gpr1, Nell1, Cep1, Bcl11, Grid 21, Htr 11, Pet100, Strip 1, Ccdc1, Skor1, Garn1, Itpkka 1, Ko 1, Kcgrp 1, Tradn 1, Ccdphn 1, Ccd3672, Cact3672, Ccdphn 1, Cact3672, Ccdnfr 1, Cdfen3672, Cbp3672, Cact3672, Cbp3672, Cbtk 1, Cact3672, Cbtp 1, Cbtk 1, Cbcn 1, Cbtk 1, Cbtp 1, Cact3672, Cbtk 1, Cbtp 1, Skt3672, Cbtp 1, Cbt. For example, the most specific genes in the nucleus of purkinje cells derived from rat samples (ordered by decreasing specificity) are: arhgef33, Car8, Slc1a6, Calb1, Ppp1r17, Itpr1, Zfp385c, Ryr1, AABR07030473.1, Slc9a3, AABR07006713.1, AABR07006713.2, RGDL561557, Gpr63, AABR07049491.1, AABR07006727.1, Plxdc1, Cep1, Fam107 1, B3gnt 1, Far 1, AC1, Grid 21, LOC1, Kcnnip 1, AABR 1, Trpc 1, Phka1, Carp 1, Fam117 1, Hes 1, Casq 1, Dgkh, Arhgap1, Trandd 21, Nepdc 1, St 361 BcB 1, Tabcb 3618 a1, Slc1a1, PtnAAtAAtAAtAAt3672, PtcTabcb 1, PtnTabcb 1, PtcTabcb 1, Tabcb.

For example, the most specific genes in the nuclei of basket cells derived from human samples (ordered by decreasing specificity) are: bhlhe22, March11, Kit, Tfp 2b, Clmp, Lbx1, Slc38a5, Frmd3, Btbd11, Lipg, Rab3b, Lrrc38, Galnt14, Cnr1, Tcerg11, Cbln 11, Siah 11, Stac 11, Scgn, Rspo 11, Lmcd 11, Lp 11, Adra 111, Gad 11, Chrna 11, Socs 11, Trpc 11, Kcna 11, SlSyn 11, Em11, Nef 11, Fam84 11, Trpc 11, KIAA 4, 11, Disp 11, Ttf 211, Adamcts 11, Cspc 11, Cspn 11, Cspc 11, Cspb 11, Cspc 11, Cspn 11, Cspf 11, Cspc 11, Cspf 11, Tspf 11, Cspf 11, Cspc 11, Cspf 11, Cspc 11, Cspf 11, Tspf 11, F11, Tspc 11, F11, Tspf 11, F11, Cspf 11, F11, Cspf 11, F11, Cspf 11, F. For example, the most specific genes in the nuclei of basket cells derived from mouse samples (ordered by decreasing specificity) are: adamts15, March11, Tfp 2b, Socs2, Penk, Bhlhe22, Plch1, Adamtsl6, Adrb2, Frmd3, Clmp, Cacnna 2d3, Trpc7, Galntl8, Tbcld4, Asgr1, Kit, Tfp 2a, Fam84a, Gjd2, Grik Gjd2, Lrrc Gjd2, Chst Gjd2, Flrt Gjd2, Chac Gjd2, Grm Gjd2, Esprrg, Slc6 sla Gjd2, Pla2g4 Gjd2, Stac Gjd2, Cabpl, Phf Gjd2, Camklg 36bl, Stc Gjd2, Sorcs Gjd2, Lamb Gjd2, Trpc Gjd2, Corp Gjd2, Scotc Gjd2, Scotr Gjd2, Csnfpr Gjd2, Cglp Gjd2, Cgln Gjd2, Cglc Gjd2, Cgln Gjd2, Cglcnk Gjd2, Cgln Gjd2, Cglc Gjd2, Cgln Gjd2, Cglc Gjd2, Cgln Gjd2, Cglc Gjd2, Cgln Gjd2, Cglc Gjd2, Cgln Gjd2, Cglc Gjd2, Cgln Gjd 2. For example, the most specific genes in the nuclei of basket cells derived from rat samples (ordered by decreasing specificity) are: plch1, AABR07001734.1, Lypd6, Lama3, March11, Gldc, Grm8, AABR07047823.1, AABR07001623.1, Gjd2, Arl4a, AABR07070578.1, Hpcal4, AABR 4, Col12 a4, Enpp4, Sorcs 4, Clmp, Plk 4, Arhgef 4, RGD1561667, Asic4, AABR 4, AC 4, Trpc 4, AABR 4, KiDct, Nt5dc 4, AABR 4, Skor 4, Prdm 4, Esrrg, Grik 4, Bhl 4, Mir346, Tm200 4, AACnbr 4, Scn 369, Acrpp 4, Tacpp 4, Tancr 4, Tanbr 4, Tabcr 4, AAGvcbr 4, Bact3672, AAGbcr 4, AACnbr 4, Bib 4, Bact3672, AAc3672, Bib 4, Babcr 4, Bact3672, Babcb 4, Babcr 4, Babcb 4, Babcr 4, Babcb 4, Bap 4, Babcr 4, Babcb 4, Babcr 4, Babcb 4, Babcr 4, Bach 4, Babcr 4, Babcb 4, Bap 4, Babcr 4, Bap 4, Babcb 4, Bap.

For example, the most specific genes in the nucleus of astrocytes derived from human samples (ordered by decreasing specificity) are: slpr1, Gja1, Lgi4, Efemp1, Etnppl, Aqp4, Rgma, Lgr6, Lcat, Tril, Serpini2, Pgghg, Slc1a3, Gfap, Aldh1a1, Pla2g5, Fxyd1, Wdr49, Gli1, Nfatc4, Scara3, Slc4a4, Lama2, Cdc42ep4, Gabrg3, P2ry1, Gabra2, Colvitec 2, Mlc 2, Slc7a 2, Aldhl, Sox 2, Lgals 2, Mgst 2, Gatsl 2, Athn 36hn, Pipox, Rhogej, Arhmbf 2, Arghbor 2, Alchl 2, Farpr 2, Fanfr 2, Farpr 2. For example, the most specific genes in the nuclei of astrocytes derived from mouse samples (ordered by decreasing specificity) are: gdf10, Slcla3, Fxyd1, Dao, A2m, Lfng, Lgi4, Mybpc1, Cdc42ep4, Slco4a1, Fam20a, Lcat, Acot11, Vim, Nwd1, Slpr1, Tnc, Rbm24, Acsbg1, Ntsr2, Prex2, Cablees 2, Gli2, Etnpp 2, Pla2g 2, Phkg 2, Npl 2, Slc14 A2, Plekho2, Pplglch 2, Slih 2, Sdc 2, Mertk, Prr 2, Sparccl 2, Mpd3672, Mphd 2, Mprcl3672, Pdd3672, Alprc 2, Alprflc 2, Alprflp 2, Sdd3672, Sdl3672, Sdrp 2, Sdcr 2, Gashr 2, Sddl 2, Sdcr 2, Eshp 2, Sdcr 2, Gashp 2, Sdcr 2, Eshp 2, Sdcr 2, Gashp 2, Eshp 2, Sdcr 2, Eshp 2, Sdcr 2, Sdc3672, Sdcr 2, Eshp 2, Sdcr 2, Eshp 2, Gashf 2, Sdc3672, Eshp 2, Gashp 2, Sdcr 2, Eshp 2, Sdc3672, Eshp 2, Sdc. For example, the most specific genes in the nuclei of astrocytes derived from rat samples (ordered by decreasing specificity) are: AABR07070161.1, Itih3, Gdf10, Gli1, Slc25a18, Nkain4, Tnc, Msx2, Slpr1, Slc7a10, Aqp9, Mt2A, Lgi4, Pou3F2, Id4, Cacng5, Cyp2d5, AABR 5, Lfng, AABR070701613, Fan107 5, Nat8F 5, Ctxn 5, Aldhll 5, Entpd 5, Rbm 5, Slc1a 5, Mlc 5, Cxcl 5, Hif 35, Fxyd 5, Acsbg 5, AABR 5, Notch 5, Il 5, cdaac 5, saxab 5, saxabr 5, saxa 5, saxabefa 5, saxabr 5, saxa 5, saxabexa 5, saxa 5, saxabr 5, saxa 5, saxabr 5, saxab 5, saxabexab 5, saxab 5, saxabexabexab 5, saxab 5, saxabexabexab 5, saxab 5, saxap 5, saxab 5, saxabexab 5, saxab 5, asgcr 5, saxab 5, asgcr 36.

For example, the most specific genes in the nuclei of oligodendrocytes derived from mouse samples (ordered by decreasing specificity) are: mog, Gldn, Cndp, Gm20425, Tmem, Slc5a, Mag, Carns, Gpr, Cdk, Hhatl, Anln, Enpp, Folh, Sec1415, Myrf, Plp, Mal, Gjb, Itga, Cpm, Plpp, Cntn, Ermn, Opalin, Abca8, Lpar, Sall, Cercam, Dock, Pi, Man2a, Pex5, Tmem63, Tmem125, Gipr, Sh3tc, D7 Er443, Kel, Ldb, Elccdc 152, Cldn, Synj, Lrp, Ninj, Psrc, Ndrg, Actn, Sgkk, Trim, Plekhhg, LaPdcn, Slc45a, Slp, Col4a, Trenk 8, Trenk, Cy, prm, Miq 3, Hafpp, Haqpp, Habpp, Habph, Habpr, Nabpp, Habph, Habpl, Nabpp, Habph, Ha, and Ha, Tm, Ha, Tp, Ha, and Habph, and Ha, and Hapf 2, and Habph. For example, the most specific genes (ordered by decreasing specificity) in oligodendrocytes derived from mouse samples are: gpr, Pex5, Hapln, Apod, Ermn, Prr5, Aspa, Efnb, Anln, Mog, Plp, Plekhh, Tmem63, Il, D7Ertd443, Secl4l, Cams, Tnfaip, Galnt, Ndrg, Cldn, Pla2G, Fth, Opalin, Car, Dock, Gm20425, Sall, 2l, Kcnk, pp1r14, Gatm, Serpinb1, Pde8, Tnni, Glu, Tmprss, Nipal, Trim, Mal, Enscl 2a, Qdpr, Litaf, Gjb, Edil, Pls, Mboftat, St6galnac, Enppp, Cpmp, Sttpip, St, Tbold, Tsaturated, Litk, Mick, Mickjk, Mickq, Michk, Michdip, Michp 4, Michp, Michk, Michp, Michmark, Michp, Michk, Michp, Michj, Michp, Michj, Mi. For example, the most specific genes in the nuclei of oligodendrocytes derived from rat samples (ordered by decreasing specificity) are: gpr 5 82 37, Pex5l, Hapln2, Apod, Ermn, Prr51, Aspa, Efnb3, Anln, Mog, Plp1, Plekhh1, Tmem63a, Il a, D7Ertd443 a, Sec14l a, Carns a, Tnfip a, Galnt a, Ndrg a, Cldn a, Pla2G a, Fth a, Opalin a, Car a, Dock a, GmSl 20425, Sall a, a l a, Kcnk a, Ppp1r a, Gatm a, Selnb 1a, Pde a, Tpde 8, Tnnn 36il, Glnpprl a, Tmp a, Tmpp a, Tm3672, Tm, Secl415, Tf, Clqb, Apod, Slpr, Rn _5_, Plp, lnsc, Mbp, Pls, Tmem, Anln, Ndrgl, PafahlB, Pex5, LOC361016, Galnt, Trim, AABR, Gpatch, Plekhg, Hamp, Csf1, Car, Trim, AABR, Gpr, Plekhh, Ghr, kdc, Slco2b, Cd, Aspa, Trpv, AABR, Aplp, Tgfbr, Ptp4a, Prima, Pik3c2, AABR, Sall, Plpp, Fam102, Cd, Cdkn1, Elovl, Piga, Inf, Blnk, Enpp, ile, Spata, mets 7, Inpp5, irjbr, trejbr, trexa, aafpr, aafpa, saqp, and paqhip 45.

For example, the most specific genes in the nuclei of OPCs derived from human samples (ranked by decreasing specificity) are: fermt1, Lims2, C1ql1, Sstr1, Olig1, Bambi, Usp43, B3gnt7, Pdgfra, C1ql2, Olig2, Fmo3, Cspg5, Galr 5, Gpc 5, Fibin, Cspg5, Col11 a5, Bche, Col9a 5, Afap112, Cldn 5, Plpp 5, Sox5, Plat, Neu 5, D5F 18Rik, Col20 a5, Pxylp 5, IC5, Spsb Ccslp 5, Prelp, Gpr 5, Best 5, Stk 5, Susdfc 5, Susdd 5, Susdhn 5, Assdrc 5, Sdcp 5, Cgpd 5, Pgpd 5, Spidct 5, Spidp 5, Cgpd 5, Spidp 5, Spidct 5, Cgph 5, Spidct 5, Sp5, Cgppr 36363672, Cgph 5, Sp5, Spidct 363672, Sp5, Spidct 3636363672, Sp5, Sp 36363672, Sp5, Sp 36363672, Sp 363672, Sp5, Spp 5, Sp 36. For example, the most specific genes in the nucleus of OPC derived from mouse samples (ranked by decreasing specificity) are: neu4, C1ql1, Ppfibp1, 3110035E14Rik, Pdgfra, Rgcc, Susd5, Itga9, Tes, Rprrm, Rep15, Cspg4, Matn4, Amz1, 181004lLl5Rik, Pxdc1, Tox3, Sh3bp4, Elfnl, Galnt3, Lmcd 3, Serpin 3, C1q 3, Netol, Grm3, Mmp 3, Coll1a 3, Sema 33, Lims 3, Cav 3, Sapcd 3, Rcn 3, PtCndd 3, Ptgfrn 3, Ptgfrd 3, Nncn, Nkan 3, Lpclat 3, Ldclat 3, Cgph 3, Csph 3, Csp3672, Csph 3, Csp3672, Csph 3, Csp3672, Cspf 3, Csph 3, Cspf 3, Csph 3, Cspf 3, C. For example, the most specific genes in the nucleus of OPC derived from rat samples (ranked by decreasing specificity) are: fam89a, Pdgfra, RGD1561849, Rlbp1, Ccnd1, Tmem255b, Clql1, Bgn, Itga9, Ppfibp1, Snx22, Col5a3, Pmel, Cav1, Serpin 2, Cspg4, Lims2, AABR07052897.1, C1ql2, Cdk2, AABR07010022.1, CaCng4, Matn4, AABR07049948.1, Susd5, Prkg2, Pxdc1, RGD1566029, AABR07003304.1, Sapcd 362, pGp 5, Mgcd 15, Rgcc Slc22 a15, Sema 515, Qprlt, Pnlip, Ppnlip 6a 15, Ctpd Slsl 6, Tanbr 15, Ctpr 15, Cgpocp 15, CgpcSt 15, Fangpsfpr 15, Fangrpc 15, Farpc 363672, Farpc 363636363672, Farpc 15, Farpc 36363672, Farpc 15, Farpc 3636363636363672, Farpc 36363672, Farpc 3636363636363672, Farpc 15, Farpc 363672, Farpc 3636363636363636363672, Farpc 15, Farpc 36363636363672, Farpc 15, Farpc 363672, Farpc 15, Farpc 363636363672, Farpc 15, Farpc 36363672, Farpc 15, Farpc 3636363636363636363672, Farpc 363672, Farpc 15.

In some embodiments, a more conservative ranking of specificity between mouse and rat is observed than between mouse and human. In some embodiments, Gene Ontology (GO) analysis may be performed on genes that are believed to be specific to each cell type or species to determine whether the genes function in the same pathway. In some embodiments, a change in expression of a cell-type or species-specific marker is observed in an alternate family member of a given cell type. For example, Pde1c can be used for mouse cell types in the cerebellum or its nuclei, and Pde1a expression can be used for human cell types in the cerebellum or its nuclei.

In some embodiments, differences in the expression levels of protein tyrosine kinase 2p (Ptk2b), solute carrier family 43 member 2(Slc43a2), the glutamate ionophore receptor rhodophytate subunit 3(Grik3), Itpr1, procalcitonin 9(Pcdh9), and Ras guanine releasing protein 1(Rasgrp1) are used to differentiate nuclei from granulocytes, glial cells, and basket/stellate cells from humans and mice.

B. Analysis of cell type-specific and species-specific Gene expression across species analysis

In some embodiments, the most variable genes are identified by comparing RNAseq results from sorted nuclei of each cell type to normalized RNAseq results for the three species. In some embodiments, cell-type specific and species specific genes can be identified by genes having one or both of the greatest variation in cell type and the greatest variation in the species analyzed. In some embodiments, it is observed that genes with the greatest expression variation between species can be further distinguished by cell type. For example, nuclei may differ more between species than cell types. In some embodiments, hierarchical clustering may be used to observe the degree of separation provided by each variant component.

In some embodiments, Pcsk2, Pcsk6, and Pcsk8 are used to classify nuclei by cell type and species. Alternatively, Pcsk1 and Pcsk3 are used to classify nuclei by cell type.

In some embodiments, the nuclear structure may be compared to differences in each cell type in regulation of gene expression or susceptibility to disease.

C. Effect of clinical Properties in human samples

In some embodiments, differences in nuclear spectra due to clinical attributes can be analyzed to identify markers of the attributes. For example, spectral differences were observed at different periods of autolysis time, sex, age and stress.

Autolysis was observed in previous studies to have only a minor effect on gene expression (Gupta et al, 2012, BMC Genomics 13, 26, which is incorporated herein by reference in its entirety). In some embodiments, expression of at least four genes in sorted nuclei can be used to define granulosa cells derived from a human sample at different stages of autolysis. In some embodiments, expression of at least one gene may be used to define basket/stellate cells or glia derived from a human sample at different stages of autolysis. In some embodiments, the expression level of kinesin family member 19(Kif19) can be measured to determine the autolysis time. For example, high expression of Kif19 indicated higher autolysis time, while lower expression level of Kif19 indicated lower autolysis time. The expression level can be compared to the normalized expression of all samples. In some embodiments, higher level expression of the FosB protooncogene, the AP-1 transcription factor subunit (FosB), indicates an intermediate autolysis time of about 5 hours to about 24 hours.

Sex-specific and cell type-specific gene expression can also be identified to determine the effect of sex on gene expression. Xist is a gene on the X chromosome that is involved in X chromosome inactivation and is expressed only in females. Next, we analyzed whether gender affected gene expression in specific cell types. Tsix is a gene antisense to Xist, which is observed to be expressed only in glia in women. This is an example of sex-specific and cell type-specific gene expression.

In some embodiments, the spectra isolated from male and female samples may be compared to determine sex-specific markers. For example, increased expression of KDM5D, RPS4Y1, ZFY, DDX3Y, TTTY15, TTTY14, USP9Y, UTY, GYG2P1, NLGN4Y, TXLNGY, LINC00278, PRKY, and/or TTTY10 compared to normalized expression in unsorted nuclei can be used as a marker for nuclei from male samples. Furthermore, reduced expression of ZFX, PUDP, KDM5C, LOC389906, PIN4, MTRNR2L8, MTRNR2L6, COL4a1, DHFR, LOC101929541, TSIX, MIR6723 and/or XIST can be used as a marker for nuclei from female samples. In some embodiments, the expression results are further analyzed to identify cell type and gender specific markers. For example, increased expression of long intergenic non-protein encoding RNA 278(LINC00278) can be used to distinguish glia from female samples from glia from male samples. Furthermore, the increase in expression of family 8 member a4, pseudogene (Fam8a4p), arylsulfatase D pseudogene 1(Arsdp1), and Gyg2p1 with sequence similarity compared to the normalized expression of unsorted nuclei can be used as a marker for nuclei from astrocytes from male samples.

to determine whether age affects cell types uniformly, differentially expressed genes can be identified in samples from people of different ages. In some embodiments, the differences in expression of the following genes in the nucleus can be used to determine the age of the subject from which the sample was obtained: ABCC10, ACAP3, ACHE, ATF6B, ATP6V0B, CACNA2D3, CUL7, DNAJB5, EGR 5, FZR 5, GBAP 5, GPR107, GRK5, KCTD 5-AS 5, KLHL5, LINC00499, LINGO 5, LOC100132077, LOC100506990, LOC101927592, MIR 5-3 HG, MPND, MTRF 5, POLR2J 5, PPP6R 5, PRSS5, PTMS, RNF 5, SNAI 5-AS 5, SPHK 5, ZST 3, TA36OK 72, TRDN, WASH 5, ZFPM 5, ZNF404, ZNF496, ZNF 36580, ZNF 36496, ZNF 36580, ZNF 36585, CAN 5, ZNF5 and ZNF 5.

In some embodiments, further analysis of the profile may provide age-specific and cell type-specific markers. For example, differential expression of the following genes can be used as markers to identify granulosa cells compared to the normalized expression of samples of different ages: KCNQ, SCHLAP, UGGT, PLXDC, ITGB, DCLK, AQP, PTPN, FAT, CMTM, FARP, PIP4K2, NEDD, CATSPERG, LOC101926, 941, WWC, MPP, SDK, NAB, NTNG, LMNTD, PLCH, PTPRJ, MRAP, PTMS, CDH, SYT, SPRED, PLXNA, GRK, ZYX, FGF, LINC01544, PCYT1, RBG, HCN, ACAP, MGMGC 1orf, SURF, TPM, FAM131, CHGB, PLXNA, ACHE, DYRK1, DIRPD 5R, GDPD, ZNF703, DC, LOC 1017, 078, CREB3L, AP1S, DYSF, MATR, KIAA 95, KITP, PDE3, ELPC 3, PLGD 3, SLC 967, SACK 2, SACK 11, SACK 53, SACK 2, SACK 53, SACK 6, SACK 1, SACK 2, SACK 2, SACK 1, SACK 6, SACK 1, SACK 7, SACK 1, SACK 2, SACK 7, SACK 1, SACK 2, SACK 1, SACK 6, SACK 1, SACK, LRRC1, RAB37, KCNIP2, RAB6B, PC, TCEAL2, MEG8, FMNL 8, MAPK8, DGCR 8, REEP 8, GTPBP 8, KIF26 8, NRXN 8, ZNRRF 8, C14orf132, FLNB, EFR 38, RBFOX 8, PPP2R 28, HDKLC 8, PILRA, CLSTN 8, CALIN 8, NHSL 8, LYLOC 8, KCNA 8, SLC38A 8, DGKA, TMEM266, FAM135 8, ATP2B 8, TMEM163, UNC 58, CACACACANCH 72, GNAO 8, GABBR 8, KCNK 360043672, USP 8, CABCAS 8, GCHATCPHASP 8, CABCAS 8, CAGCHATC 8, CAGCCAGCHATC 8, CAGCHATC 8, CANCP 8, CAGCCAGCCAGCCAGCHATC 8, CAGCCAGCCAGCCANCP 8, CAGCCAGCCANCP 8, CANCP 8, CANC3672, CANC363636363672, CANC363636363636363636363636363636363636363672, CANC363672, CANC3672, MEF2C-AS1, BMS1P21, CASP12, CLMP, ERBIN, TMC3-AS1, CXXC4, DCBLD1, GSTO2, PLOD2, P4HA2-AS1, TTC23L, CPLX3, SOX13, PRDM1, RSPO2, TSG1, MIR31HG, CTB-12O2.1, PLCH1, JAML, SEMA3G, LOC101928, 203, GLRA1, SYT16, SYN3, ARHGAP32, MIR646HG, ALCAM, SCGN, LINC01515, and LOC 101929415.

For example, differential expression of the following genes can be used as markers for identifying basket cells compared to the normalized expression of samples of different ages: ACER3, ACSS3, ADAMTS2, ADAMTTSL 2, ADD2, ADGRL2, AKAP 2, ALOX 2, ALS2CR 2, ARHGEF10 2, ARHGEF2, ASIC2, ATP6V 02, BMP 2, BTBD 2, C11orf 2, C14orf 2, C4orf2, C9orf135, CA2, CACNA 12, CACNA2D2, CACNNB 2, CADM 2, CADPS 360043672, CFCALAP 2, CLSTN 2, CLCNTN 2, CPNE 2, CYYRR 2, MYDCC, QSDGNO, KHDNAS 2, DISP2, GALG 360043672, GCG 2, DMGCSHGC3672, GCSHGCSHGCSHGCS 2, GCSHGCRONFS 2, GCS 2, GCRONFS 2, GCSHGCRONFR 2, GC3672, GCRONFS 2, GCRONFR 2, GC3672, GCRONFR 2, GC3672, GCSHRONFR 2, GC3672, GCRONFR 2, GCSHGCRONFR 2, GC3672, GCRONFR 2, GC3672, GCRONFR 2, GC3672, GCRONFR 2, GC3672, GCRONFR 2, GC3672, GCRONFR 2, GC3672, GCRONFR 2, GC3672, GCRONFR, ST6GAL2, ST6GALNAC5, STAT5A, STK33, SV2C, SYT17, TEX9, TFAP2B, THSD7B, TMEM178B, TRAF5, TRIM67, TTC23L, TUNAR, UBASH3B, UNC5D, ZNF124, and ZYG 11A.

For example, differential expression of the following genes can be used as markers for identifying glial cells compared to the normalized expression of samples of different ages: ADAMTS7, ANKRD33B, AOx1, C1orf95, CACNG4, CACNG8, CAPN9, CRTAC1, DACH1, EFHD2, EGF, ETS1, FRAS1, IMPA2, LINC00499, LINC00844, LINC01208, LOC101927078, LOC 721024360, PKDCC, PLXNA4, PRR5, RHCG, ROBO2, SEMA5B, SHROOM2, SLIT2, TRDN, TXINIP, VWA5A and WNT 7B.

In some embodiments, gene expression in the nucleus

D. Selection markers and targets

In some embodiments, the analysis of altered expression associated with sorted nuclei occurs between gene orthologs between different species. For example, expression of 1: 1 orthologs was compared to ensure that the results were due to altered expression rather than differences in gene annotation among species.

In some embodiments, further profiling may be performed to ensure that altered expression is not due to annotated differences between species. For example, a gene with only a 1: 1 ortholog can be used to determine expression levels. Profiling can be additionally limited to genes that are greater than 1kb in length and/or have less than a 2-fold difference in length between species (e.g., mouse, rat, and human). In some embodiments, the cell-type specific or species-specific genes may be selected from the 250 most variable genes in mouse, rat, and human nuclei, including: l15Rik, M11Rik, E14Rik, A2, Adamts, Adra1, AI593442, Aldh1a, Anln, Apcdd, Apod, Aqp, Arhgef, Ascl, Asgr, Aspa, Atp1a, 2a, B3gnt, Bcl11, Bhlhe, Clql, C1ql, Cacnal, Cacng, Calb, Camk1, Carn, Casq, Cbln, Ccdc152, Ccdc180, Ccnd, Cd, Cdh, Clec Cl7, Clmn, Ctm, Cntn, Cntnap5, Cobl, Col5a, Col6a, Cpm, Cryab, Csp, Csd, D7, E3, E Rik, A2, Adapl, Ilh, Ilfq, C1, C, Cdh, Cdr, C < 7, Cdl, Cdr, Cdl, Cmd, Ctm, Cmd, Ctm, Cdt, Ilf < 7, Ill < g < III, G < III, G < III, G < III, G < III, G < III > C < III, G < III >, Marchl1, Marcksl1, Mcam, Mff, Mfge8, Mlc1, Mog, mt-Co1, mt-Nd4, Mybpc1, Myot, Myrf, Ndrg1, Nell1, Neo 1, Neu 1, Ninj 1, Nkx 1-2, Notch1, Nrk, Olig1, Opalin, Pax 1, Ppp1, Ppdc, Pdgfra, Pdzd 1, Penk, Pex51, Pgghg, Phyhip, Pkp 1, Pla2g1, Pprgl3672, Ppekp 1, Ptlp 1, Pplglpr 1, Pplkp 1, Pplglpp 1, Prxpl 1, Pprplzp 1, Pprplpr 1, Pplzp 1, Pprplzp 1, Pprplpr 1, Pprplzp 1, Pprplp 1, Pprprprprprprplp 1, Pprprprplp 1, Pprplp 1, Pprprprprprprprprprpr, qk, Rapg, Rassf, Rbp, Rbpj, Rgcc, Rgma, Ryr, S100, Slpr, Sall, Sec14l, Serpine, Shisa, Skor, Slc1a, Slc26a, Slc32a, Slc4a, Slc5a, Slc9a, Slitrk, Socs, Sorcs, Sox, Stag, Stk32, Sulf, Susd, Tcea, Tmap 2, Thbs, Tmem125, Tmem132, Tmem63, Tmem, Tnc, Tnfrsf13, Tns, Trbd 2, Tri, Trpc, Tshb, Tspan, Tuba, Tubb2, Tnip, Ubcp 2, Wrtzbp 2, Zpc, and Zfb.

Adjusting the identified target

In some embodiments, at least one gene selected from one of tables 2-4 is associated with at least one disease, disorder, or condition. A gene or its expression associated with a disease, disorder or condition may also be referred to as a disease target. In some embodiments, methods of treating diseases, disorders, conditions, or disorders comprise using a therapeutic modality to modulate the expression of a disease target. In some embodiments, the therapeutic modality targets at least one transcript selected from tables 2-4. In some embodiments, the therapeutic modality targets at least one protein selected from tables 2-4. In some embodiments, the treatment modality is a pharmaceutical composition (e.g., a small molecule drug or an RNA interference (RNAi) molecule (e.g., siRNA)), a gene therapy, or a cell therapy.

Definition of VI

The following is a non-limiting list of term definitions.

as used herein, the term "about," when applied to one or more values of interest, refers to a value that is similar to the referenced value. In certain embodiments, the term "about" or "about" refers to a range of values that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1%, or less of either direction (greater than or less than) of the stated reference value, unless otherwise stated or apparent from the context (unless such number exceeds 100% of the possible value).

As used herein, the term "antibody" refers to an immunoglobulin molecule that is capable of specifically binding to a target (e.g., a polynucleotide, polypeptide, protein, etc.) through at least one antigen recognition site located in the variable region of the immunoglobulin molecule. The term "antibody" includes not only intact (e.g., full-length) polyclonal or monoclonal antibodies, but also antigen-binding fragments thereof (e.g., Fab ', F (ab') 2, Fv), single chains (scFv), mutants thereof, fusion proteins comprising an antibody portion, humanized antibodies, chimeric antibodies, diabodies, linear antibodies, single chain antibodies, multispecific antibodies (e.g., bispecific antibodies), and any other modified configuration of an immunoglobulin molecule comprising an antigen recognition site of a desired specificity, including glycosylated variants of an antibody, amino acid sequence variants of an antibody, and covalently modified antibodies. Antibodies include any class of antibody, e.g., IgD, IgE, IgG, IgA, or IgM (or subclasses thereof), and antibodies need not be of any particular class.

As used herein, the term "cell specific profiling" refers to the use of the nuclear transcriptome to accurately define cell identity.

As used herein, the term "Chromosome Associated Transcript (CAT)" refers to a transcript that remains on the chromosome when synthesized.

As used herein, the term "disease tissue" or "disease sample" refers to a sample taken from a subject experiencing a disease state.

as used herein, the term "DNA probe" or "RNA probe" refers to a fragment of DNA or RNA (typically 100-1000 bases long) that hybridizes to a nucleotide sequence complementary to the probe sequence. The probe may be radiolabeled, fluorescently labeled or chemically (e.g., biotin) for visualization.

As used herein, the term "drug target" or "therapeutic target" refers to a nucleotide sequence that can be acted upon by a drug or therapeutic agent to modulate gene expression in a cell type.

As used herein, the term "agent" refers to a nucleotide sequence that is hybridized by a DNA or RNA probe or specifically bound by an antibody to allow for precise sorting of nuclei of a particular cell type.

As used herein, the term "gene expression" or "expression" refers to the level of one or more of the following events: (1) generating an RNA template from the DNA sequence (e.g., by transcription); (2) processing of RNA transcripts (e.g., by splicing, editing, 5 'cap formation, and/or 3' end processing); (3) translation of RNA into a polypeptide or protein; (4) folding of the polypeptide or protein; and (5) post-translational modifications of the polypeptide or protein.

As used herein, the term "cellular profiling technique" refers to a non-genetic method for molecular profiling a population of cell types in post-mortem tissue.

As used herein, the term "poly a transcript" refers to an adenine nucleotide chain that is added to a messenger RNA (mrna) molecule during RNA processing to increase the stability of the molecule.

As used herein, the term "label" refers to one or more markers, signals, or moieties linked, introduced, or associated with another entity, which are readily detectable by methods known in the art, including radiography, fluorescence, chemiluminescence, enzyme activity, absorbance, and the like. Detectable affinity labels include radioisotopes, fluorophores, chromophores, enzymes, dyes, metal ions, ligands such as biotin, avidin, streptavidin and haptens, quantum dots, and the like. The detectable affinity label can be located anywhere on the entity to which it is attached, incorporated, or associated. For example, when linked, incorporated or associated to a peptide or protein, they may be internal to the amino acid, peptide or protein, or located at the N-terminus or C-terminus.

As used herein, the term "most specific gene" refers to a gene having a lower Specificity Index (SI) value than the SI values of other genes analyzed, which is calculated using the algorithm described in example 1.

As used herein, the term "tissue sample" refers to an aliquot or portion taken from a source and provided for analysis or processing. In some embodiments, the sample is from a biological source, such as a tissue, cell, or component (e.g., a bodily fluid, including but not limited to blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen). In some embodiments, the sample may be or comprise a homogenate, lysate or extract prepared from the whole organism or a subset of its tissues, cells or components, or fractions or portions thereof, including but not limited to, for example, plasma, serum, spinal fluid, lymph fluid, external skin, respiratory, intestinal and genitourinary tracts, tears, saliva, milk, blood cells, tumors, organs. In some embodiments, the sample is or comprises a culture medium, such as a nutrient broth or gel, which may contain cellular components, such as proteins or nucleic acid molecules. In some embodiments, the sample is subjected to one or more processing (e.g., separation, purification, etc.) steps to prepare the sample for analysis or other use.

As used herein, the term "transgenic animal" refers to an animal having an exogenous gene inserted into its genome.

As used herein, the term "Translational Ribosome Affinity Purification (TRAP)" refers to a method of defining the molecular characteristics of different cell types in a single tissue from a transgenic animal.

described herein are methods for molecular profiling of a population of cell types in post-mortem tissue. The details of one or more embodiments of the invention are set forth in the description below. Although any materials and methods similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred materials and methods are now described. Other features, objects, and advantages of the invention will be apparent from the description. In the specification, an indefinite article "a" or "an" includes one and/or more than one unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In case of conflict, the present specification will control.

The invention is further illustrated by the following non-limiting examples.

476页详细技术资料下载

上一篇：一种医用注射器针头装配设备

下一篇：相对于相匹配的正常cfRNA的肿瘤

Methods for cell type specific profiling to identify drug targets

相关技术

网友询问留言