Methods for determining spatial and temporal gene expression dynamics in single cells

Patent metadata

Field	Value
Publication number	US-12060412-B2
Application number	US-201616087036-A
Country	US
Kind code	B2
Filing date	Oct 27, 2016
Priority date	Mar 21, 2016
Publication date	Aug 13, 2024
Grant date	Aug 13, 2024

Abstract

Transcriptomes of individual neurons provide rich information about cell types and dynamic states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation from dense adult tissue is challenging, and markers for each phase are limited. Here, Applicants developed Nuc-seq, Div-Seq, and Dronc-Seq. Div-seq combines Nuc-Seq, a scalable single nucleus RNA-Seq method, with EdU-mediated labeling of proliferating cells. Nuc-Seq can sensitively identify closely related cell types within the adult hippocampus. Div-Seq can track transcriptional dynamics of newborn neurons in an adult neurogenic region in the hippocampus. Dronc-Seq uses a microfluidic device to co-encapsulate individual nuclei in reverse emulsion aqueous droplets in an oil medium together with one uniquely barcoded mRNA-capture bead. Finally, Applicants found rare adult newborn GABAergic neurons in the spinal cord, a non-canonical neurogenic region. Taken together, Nuc-Seq, Div-Seq and Dronc-Seq allow for unbiased analysis of any complex tissue.

First claim

What is claimed is:

1. A method of producing a temporally phased single-cell sequencing library of at least one cell type or subtype, wherein said sequencing library comprises cells along a continuous trajectory of cell developmental stages comprising:
(a) treating more than one population of cells of a single cell type or subtype, or optionally a heterogeneous cell type, with a nucleoside analogue, wherein the nucleoside analogue is incorporated into replicating DNA and is configured for labeling with a detectable marker;
(b) isolating a first population of cells at one time point and isolating at least one other population of cells at a later time point, and isolating single nuclei from the isolated populations of cells;
(c) staining the nucleoside analogue incorporated into replicated DNA with the detectable marker within each population of single nuclei isolated from each population of cells, wherein the DNA is stained with the detectable marker;
(d) sorting stained and/or unstained single nuclei into separate reaction vessels; and
(e) sequencing RNA from the sorted single nuclei, whereby single cell gene expression data is obtained for cells at different stages of maturation.

2. The method according to claim 1 , wherein the treating more than one population of cells of a single cell type or subtype, or optionally a heterogeneous cell type with a nucleoside analogue is performed in at least one subject.

3. The method according to claim 2 , wherein the subject is a mouse.

4. The method according to claim 2 , wherein isolating one population of cells comprises dissection of a tissue from the subject, or the population of cells is grown in tissue culture, and wherein the tissue is optionally nervous tissue in particular nervous tissue isolated from brain, spinal cord, or retina or immune cells.

5. The method according to claim 1 , wherein the gene expression data is obtained from single cell sequencing, or wherein the gene expression data is obtained from single nucleic sequencing.

6. The method according to claim 5 , wherein single nuclei sequencing comprises:
(a) treating the populations of cells with a reagent that stabilizes RNA;
(b) extracting nuclei from the cells;
(c) sorting single nuclei into separate reaction vessels;
(d) extracting RNA from the single nuclei;
(e) generating a cDNA library; and
(f) sequencing the library,
whereby gene expression data from single cells is obtained.

7. The method according to claim 6 , wherein single nuclei are sorted into single wells of a plate by FACS or wherein the sorting comprises microfluidics.

8. The method according to claim 5 , wherein single nuclei sequencing comprises a method of high-throughput single nuclei sequencing, said method comprising:
(a) treating the population of cells with a reagent that stabilizes RNA;
(b) extracting nuclei;
(c) generating a suspension of isolated nuclei, wherein the suspension comprises a nuclear pore blocking polymer;
(d) optionally, enriching the nuclei suspension by FACS or magnetic-activated cell sorting (MACS);
(e) applying the nuclei suspension to a reverse emulsion microfluidic device configured for single nuclei, wherein single nuclei are individually compartmentalized with a single uniquely barcoded capture bead in an emulsion drop;
(f) extracting mRNA onto the barcoded capture beads;
(g) generating a barcoded cDNA library; and
(h) sequencing the library using paired-end sequencing,
whereby gene expression data from single nuclei is obtained.

9. The method according to claim 8 , wherein the nuclei suspension comprises 10 5 -10 6 nuclei or wherein 10 4 -10 5 nuclei are sequenced.

10. The method according to claim 8 , wherein the nuclear pore blocking polymer comprises a poloxamer, or wherein the reagent comprises an RNA preservation agent, or both.

11. The method according to claim 1 , further comprising producing at least one high resolution map for visualizing a temporal position or cell developmental stage of cells of a specific cell type, subtype or cell state during proliferation comprising:
(a) performing dimensionality reduction on the single cell gene expression data from the stained cells of a single cell type, subtype or cell state within each population of cells or the stained single nuclei of a single cell type or subtype isolated from each population of cells;
(b) measuring dissimilarity between sets of genes in the dimensionality reduced single cell gene expression data and applying a first metric, whereby a continuous trajectory is visualized in dimensionality reduced space from an early time point to a later time point;
(c) producing a set of informative genes by a method comprising scoring genes based on their expression across the continuous trajectory, wherein the informative genes are uniquely expressed in cells embedded in close proximity in the dimensionality reduced space, optionally, wherein lowly expressed genes are filtered out; and
(d) producing at least one set of clusters of cells by a method comprising measuring the dissimilarity between the set of informative genes and applying a second metric, whereby visualization of the set of clusters in the dimensionality reduced space indicate gene expression profiles of cells based on a temporal position or developmental stage.

12. The method according to claim 11 , wherein producing the set of clusters of cells in step (d) comprises producing more than one set of clusters, wherein the first set of clusters is produced by using a highest scoring informative gene and each successive set of clusters is produced by adding a next highest scoring informative gene.

13. The method according to claim 11 , further comprising a) normalization of the single cell gene expression data, wherein gene expression of one cell is normalized to another using not highly expressed genes, or b) estimation of missed detection probability, wherein an expectation maximization algorithm is applied, or both a) and b).

14. The method according to claim 11 , wherein scoring informative genes comprises applying a Moran's I analysis and/or a Manhattan distance analysis, or wherein dimensionality reduction comprises PCA and/or tSNE, or both.

15. The method according to claim 1 , wherein the nucleoside analogue comprises EdU (5-ethynyl-2′-deoxyuridine).

16. The method of claim 11 , further comprising first performing a method of producing at least one high resolution map for visualizing different cell subtypes or cell states in a heterogeneous population of cells.

17. The method of claim 16 , wherein the step of performing a method of producing at least one high resolution map for visualizing different cell subtypes or cell states in a heterogeneous population of cells comprises:
(i) performing dimensionality reduction on single cell gene expression data obtained from the heterogeneous population of cells;
(ii) producing a first set of clusters of cells by a method comprising measuring the dissimilarity between sets of genes in the dimensionality reduced single cell gene expression data and applying a first metric, wherein the clusters are in a dimensionality reduced space and the clusters comprise cells with a continuous trajectory;
(iii) producing a set of informative genes by a method comprising scoring genes based on their expression across the first set of clusters of cells or a continuous trajectory of cells, wherein the informative genes are uniquely expressed in cells embedded in close proximity in the dimensionality reduced space; and
(iv) producing at least one second set of clusters of cells or continu

Assignees

• Broad Inst Inc
• Massachusetts Inst Technology
• Harvard College
• President And Fellows Of Harvard College Ma

Inventors

• Regev Aviv
• Zhang Feng
• Habib Naomi
• Li Yinqing
• Heidenreich Matthias
• Swiech Lukasz
• Basu Anindita
• Weitz David
• Davidi Inbal Avraham

Classifications

• C07K16/112 Primary

  Retroviridae (F), e.g. leukemia viruses · CPC title

• C12Q2565/629

  being a microfluidic device · CPC title

• C12Q2565/514

  characterised by the use of the arrayed oligonucleotides as identifier tags, e.g. universal addressable array, anti-tag or tag complement array · CPC title

• C12Q2563/179

  the label being a nucleic acid · CPC title

• C12Q2537/165

  Mathematical modelling, e.g. logarithm, ratio · CPC title