METHOD FOR DETERMINATION AND IDENTIFICATION OF CELL SIGNATURES AND CELL MARKERS

• US 20180127823A1
• Filed: 08/17/2017
• Published: 05/10/2018
• Est. Priority Date: 08/17/2016
• Status: Active Grant

• Current Assignee: President and Fellows of Harvard College (Harvard Corporation), Howard Hughes Medical Institute, Massachusetts Institute of Technology, The Broad Institute, Inc.
• Original Assignee: President and Fellows of Harvard College (Harvard Corporation), Massachusetts Institute of Technology, The Broad Institute, Inc.
• Inventors: SHEKHAR, Karthik, LAPAN, Sylvain, WHITNEY, Irene, MACOSKO, Evan, MCCARROLL, Steven, CEPKO, Constance, REGEV, Aviv, SANES, Joshua
• Granted Patent: US 11,225,689 B2
• Time in Patent Office: Days
• CPC Class Codes: C12N 5/0621 Eye cells, e.g. cornea, iris pigmented cells photoreceptors C12N5/062
• CPC Class Codes: C12Q 1/6841 In situ hybridisation
• CPC Class Codes: C12Q 1/6881 for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G01N 2800/164 Retinal disorders, e.g. retinopathy
• CPC Class Codes: G01N 33/56966 Animal cells
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

The present invention provides for methods of identifying cell types and cell subtypes from a biological sample or population of target cells. The methods further provide for determining cell type or cell subtype signatures. The method further provides for bipolar cell subtypes and markers and cell signatures thereof.

Claims

1-60. -60. (canceled)

61. A method of identifying a cell or cell marker, comprising

a) isolating target cells based on a marker specifically expressed in the cell or by label-free imaging flow cytometry;

b) quantifying gene expression in the target cells by sequencing, and

c) clustering the target cells based on the gene expression by application of one or more algorithms, and

d) optionally determining a transcription signature for each cluster based at least in part on identifying differentially expressed genes between two or more clusters and between each cluster and the remaining cells as background, and

e) optionally validating gene expression against cellular morphology.

62. The method of claim 61, wherein the marker is a fluorescent transgene specifically expressed by the target cells.

63. The method of claim 62, wherein the transcription signature identifies a bipolar cell type.

64. The method of claim 63, wherein the transcription signature identifies a rod bipolar cell, or one of the following cone bipolar cells;

BC1A, BC1B, BC2, BC3A, BC3B, BC4, BC5A, BC5B, BC5C, BC5D (XBC), BC6, BC7, BC8, and BC9.

65. The method of claim 64, wherein the transcription signature identifies a bipolar cell and comprises at least one of Pcdh17, Pcdh10, Erbb4, Nnat, Col11a1, Sox6, Chrm2, Slitrk5, Lrrtm1, Cck, Lect1, Igfn1, Serpini1, Cpne9, Vstm2b, Casp7, Vsx1, Vsx2, Otx2, Scgn, Slc1a2, Slc6a9, Pax6, Tfap2a, Stx1b, Tacr3, Bhlhe23, Fat4, Fezf1, Fezf2, Wfdc2, Mylk, Hs3st2, Wls, Nxph1, Lhx3, Cabp5, Grm6, Kcng4, Cdh9, Htr3a, Hcn1, Hcn4, Nfia, Cntn5, BC046251, Gfra3, Areg, Kirrel3, Isl1, Spock3, Serpini1, Prkca, Car8, Sebox, Apoe, GluI, Aqp4, Cabp5, Syt2, Grik1, Prkar2b, Rcvrrn, Irx6, Gad1, Rho, Pdc, Nr1, Pde6a, Pde6h, Arr3, Opn1mw, and Opn1sw.

66. The method of claim 61, wherein optionally validating gene expression against cellular morphology comprises sparse labeling the cell to enhance the expression of a fluorescent protein in the cell and combining the sparse labeling with fluorescent in situ hybridization (FISH) to validate the marker against cellular morphology in step e).

67. The method of claim 66, wherein FISH is combined with a specific antibody, double FISH or a transgenic reporter mouse line directed to a previously identified marker in the cell.

68. A method of determining transcription signatures for cell types or cell sub-types, comprising:

identifying cell types and/or cell sub-types by applying one or more clustering algorithms to a set of sequenced transcript reads from a population of cells, wherein each resulting cluster of sequenced transcript reads identifies a particular cell type or cell sub-type; and

determining a transcription signature for each identified cell type or cell sub-type based at least in part on identifying differentially expressed transcripts between the identified cell type or cell sub-type.

69. The method of claim 68, wherein identifying differentially expressed transcripts comprises application of a supervised or unsupervised machine-learning model.

70. The method of claim 69, wherein the supervised machine learning model is an analytical learning model, an artificial neural network model, a back propagation model, a boosting model, a Bayesian statistics model, a case-based model, a decision tree learning model, an inductive logic programming model, a Gaussian process regression model, a group method of data handling model, a kernel estimator model, a learning automata model, a minimum message length model, a multilinear subspace learning, a naive bayes classifier model, a nearest neighbor model, a probably approximately correct (PAC) learning model, a ripple down rules model, a symbolic machine learning model, a subsymbolic machine learning model, a support vector machine learning model, a minimum complexity machine model, a random forest model, an ensemble of classifiers model, an ordinal classification model, a data pre-processing model, a handling imbalanced datasets model, a statistical relational learning model, a Proaftn model, and wherein the unsupervised machine learning model is a k-means model, a mixture model, a hierarchical clustering model, an anomaly detection model, a neural network model, an expectation-maximization (EM) model, a method of moments model, or a blind signal separation technique.

71. The method of claim 68, wherein the sequenced transcript reads are obtained from RNA sequencing of 20,000 or more single cells from a biological sample, wherein the sequencing depth is about 10,000 or less reads per cell.

72. The method of claim 68, wherein the cell type and/or sub-types are retinal cell types and/or sub-types.

73. The method of claim 72, wherein the transcription signature identifies a rod bipolar cell, or one of the following cone bipolar cells;

BC1A, BC1B, BC2, BC3A, BC3B, BC4, BC5A, BC5B, BC5C, BC5D (XBC), BC6, BC7, BC8, and BC9.

74. The method of claim 73, wherein the transcription signature identifies a bipolar cell and comprises at least one of Pcdh17, Pcdh10, Erbb4, Nnat, Co11a1, Sox6, Chrm2, Slitrk5, Lrrtm1, Cck, Lect1, Igfn1, Serpini1, Cpne9, Vstm2b, Casp7, Vsx1, Vsx2, Otx2, Scgn, Slc1a2, Slc6a9, Pax6, Tfap2a, Stx1b, Tacr3, Bh1he23, Fat4, Fezf1, Fezf2, Wfdc2, Mylk, Hs3st2, Wls, Nxph1, Lhx3, Cabp5, Grm6, Kcng4, Cdh9, Htr3a, Hcn1, Hcn4, Nfia, Cntn5, BC046251, Gfra3, Areg, Kirrel3, Isl1, Spock3, Serpini1, Prkca, Car8, Sebox, Apoe, GluI, Aqp4, Cabp5, Syt2, Grik1, Prkar2b, Rcvrrn, Irx6, Gad1, Rho, Pdc, Nrl, Pde6a, Pde6h, Arr3, Opn1mw, and Opn1sw.

75. A method for cell perturbation screening, comprising:

exposing a test cell population to one or more perturbations;

detecting one or more cell transcription signatures in a sample by sequencing of transcripts expressed in the cell population, wherein the one or more transcription signatures indicates an absence or presence of a particular cell type or cell sub-type in the sample.

76. The method of claim 75, wherein the one or more perturbations comprises exposing the sample to one or more chemical agents, a concentration range of one or more chemical agents, or both.

77. The method of claim 75, wherein the test cell population is a retina cell population.

78. The method of claim 77, wherein the absence or presence of a particular cell type or cell sub-type indicates a propensity of the one or more perturbations to cause blindness.

79. An isolated bipolar cell, wherein the bipolar cell is a rod bipolar cell, or one of the following cone bipolar cells;

BC1A, BC1B, BC2, BC3A, BC3B, BC4, BC5A, BC5B, BC5C, BC5D (XBC), BC6, BC7, BC8, and BC9.

80. The isolated bipolar cell of claim 79, wherein;

a. the BC1A cell is characterized by expression of Pcdh17 and Fezf2;

b. the BC1B cell is characterized by expression of Pcdh10 and Fezf1;

c. the BC5A cell is characterized by expression of Sox6, B046251, Cntn5, Lrrtm1, Nnat, Htr3a, Kcng4, and Nfia;

d. the BC5B cell is characterized by expression of Pcdh17, Nfia, Cntn5, Gfra3, Chrm2, and Nnat;

e. the BC5C cell is characterized by expression of Pcdh17, Pcdh10, Cntn5, Slitrk5 and Areg;

f. the BC5D cell is characterized by expression of Nnat, B046251, Kirrel3, Htr3a and Kcng4, Lact1, Lrrtm1 and Nfia;

g. the BC8 cell is characterized by Serpini⁺ Cpne⁻

;

h. the BC9 cells is characterized by Serpini⁺ Cpne⁺ and optionally an exclusion zone of 14.3 μ

m where no other Cpne⁺ cells are positioned.