Assays for massively combinatorial perturbation profiling and cellular circuit reconstruction

Patent metadata

Field	Value
Publication number	US-11214797-B2
Application number	US-201815965192-A
Country	US
Kind code	B2
Filing date	Apr 27, 2018
Priority date	Oct 28, 2015
Publication date	Jan 4, 2022
Grant date	Jan 4, 2022

Abstract

The present invention provides tools and methods for the systematic analysis of genetic interactions, including higher order interactions. The present invention provides tools and methods for combinatorial probing of cellular circuits, for dissecting cellular circuitry, for delineating molecular pathways, and/or for identifying relevant targets for therapeutics development.

First claim

What is claimed is:

1. A method for pooled screening of perturbations with single cell readouts comprising
(a) introducing one or more perturbation constructs encoding for one or more sequence specific perturbations to a plurality of cells in a population of cells, wherein each cell in the plurality of the cells receives at least 1 perturbation and wherein each perturbation construct encodes for an RNA sequence comprising a sequence identifying the perturbation and a polyadenylation sequence;
(b) detecting endogenous mRNAs and the sequence identifying the perturbation for each single cell in the plurality of cells using single cell RNA-seq; and
(c) determining measured differences in the endogenous mRNAs that are correlated to the sequence specific perturbations detected for each single cell.

2. The method of claim 1 , further comprising estimating the impact of the perturbations on the single cells by applying a computational model accounting for mRNA copy number in each single cell, whether the perturbation actually perturbed the cell; the presence of subpopulations of either different cells or cell states, and/or analysis of cells in the population of cells without any perturbation.

3. The method of claim 1 , wherein the one or more sequence specific perturbations comprise 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 perturbations; and/or
wherein the perturbation(s) target genes in a pathway or intracellular network.

4. The method of claim 1 , wherein the perturbation(s) comprise one or more genetic perturbations selected from the group consisting of gene knock-down, gene knock-out, gene activation, gene insertion, and regulatory element deletion; and/or
one or more epigenetic or epigenomic perturbations.

5. The method of claim 1 , wherein at least one sequence specific perturbation is introduced with RNAi or a CRISPR-Cas system.

6. The method of claim 5 , wherein the one or more sequence specific perturbations are one or more sgRNAs introduced with a library of CRISPR-Cas sgRNAs.

7. The method of claim 6 , wherein each sequence identifying the perturbation is a unique perturbation barcode that identifies the sgRNA introduced to a cell.

8. The method of claim 6 , wherein the sequence identifying each perturbation is the same sgRNA sequence for each sgRNA introduced with the library of CRISPR-Cas sgRNAs.

9. The method of claim 1 wherein the population of cells comprises ex vivo or in vitro cells.

10. The method of claim 1 , wherein each cell is in a microfluidic system; and/or wherein each cell is in a droplet.

11. The method of claim 1 , wherein the one or more sequence specific perturbations comprise one or more genomic sequence-perturbations.

12. The method of claim 1 , further comprising determining proteins expressed in the single cells, wherein the proteins are detected in the single cells by binding of more than one labeling ligands comprising an oligonucleotide tag, wherein the oligonucleotide tag comprises a unique constituent identifier (UCI) specific for a target protein.

13. The method of claim 8 ,
wherein single cells are fixed in discrete particles; and/or
wherein the labeling ligand comprises an oligonucleotide label comprising a regulatory sequence configured for amplification by T7 polymerase; and/or
wherein the oligonucleotide label further comprises a photocleavable linker; and/or
wherein the oligonucleotide label further comprises a restriction enzyme site between the labeling ligand and unique constituent identifier (UCI); and/or
further comprising quantitating relative amount of UCI sequence associated with a first cell to the amount of the same UCI sequence associated with a second cell, whereby the relative differences of a cellular constituent between cell(s) are determined.

14. The method of claim 12 , wherein the labeling ligands are antibodies or antibody fragments selected from the group consisting of a nanobody, Fab, Fab′, (Fab′)2, Fv, ScFv, diabody, triabody, tetrabody, Bis-scFv, minibody, Fab2, and Fab3 fragment; or wherein the labeling ligands are aptamers.

15. The method according to claim 1 , further comprising comparing the mRNAs of each perturbed cell with any mutations in the perturbed cells to also correlate mRNA profile and genotypic profile.

16. The method according to claim 1 , further comprising determining genetic interactions by causing a set of P genetic perturbations in single cells of the population of cells,
wherein the method comprises:
(a) determining, based upon random sampling, a subset of π genetic perturbations from the set of P genetic perturbations;
(b) performing said subset of π genetic perturbations in a population of cells;
(c) performing single-cell molecular profiling of the population of genetically perturbed cells of step (b);
(d) inferring, from the results of step (c) and based upon the random sampling of step (a), single-cell molecular profiles for the set of P genetic perturbations in cells; and optionally,
(e) from the results of step (d), determining genetic interactions; and further optionally,
(f) confirming genetic interactions determined at step (e) with additional genetic manipulations.

17. The method according to claim 16 ,
wherein said set of P genetic perturbations or said subset of π genetic perturbations comprises single-order genetic perturbations; or
wherein said set of P genetic perturbations or said subset of π genetic perturbations comprises combinatorial genetic perturbations; or
wherein said set of P genetic perturbations or said subset of π genetic perturbations comprises genome-wide perturbations; or
wherein said set of P genetic perturbations or said subset of π genetic perturbations comprises k-order combinations of single genetic perturbations, wherein k is an integer ranging from 2 to 15, and wherein step (e) comprises determining k-order genetic interactions; or
wherein said set of P genetic perturbations comprises combinatorial genetic perturbations, such as k-order combinations of single-order genetic perturbations, wherein k is an integer ranging from 2 to 15, and wherein step (e) comprises determining j-order genetic interactions, with j<k.

18. The method according to claim 16 , wherein step (b) comprises performing RNAi- or CRIPSR-Cas-based perturbation; and/or
wherein step (b) comprises pool-format perturbation; and/or
wherein step (b) comprises pooled single or combinatorial CRISPR-Cas-based perturbation with a genome-wide library of sgRNAs; and/or
wherein step (b) comprises pooled combinatorial CRISPR-Cas-based perturbation with a genome-wide library of sgRNAs.

19. The method according to claim 16 , wherein random sampling comprises matrix completion, tensor completion, compressed sensing, or kernel learning; or
wherein random sampling comprises matrix completion, tensor completion, or compressed sensing, and wherein π is of the order of log P.

20. The method of claim 1 , wherein the population of cells is a population of eukaryotic cells.

21. The method of claim 20 , wherein the eukaryotic cells are mammalian cells.

22. The method of claim 21 , wherein the mammalian cells are human cells.

Assignees

• Broad Inst Inc
• Massachusetts Inst Technology
• Univ California

Inventors

• Regev Aviv
• Adamson Brittany
• Cleary Brian
• Cong Le
• Dixit Atray
• Gaublomme Jellert
• Lander Eric S
• Norman Thomas
• Parnas Oren
• Rozenblatt-Rosen Orit
• Shalek Alexander K
• Weissman Jonathan

Classifications

• C12Q2600/106

  Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism · CPC title

• C12Q2600/136

  Screening for pharmacological compounds · CPC title

• C12N2800/80

  Vectors containing sites for inducing double-stranded breaks, e.g. meganuclease restriction sites · CPC title

• G16B5/00

  ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks · CPC title

• C12N15/1093 Primary

  General methods of preparing gene libraries, not provided for in other subgroups · CPC title