Decoding Heterogenous Single-cell Perturbation Responses

Bicna Song, Center for Genetic Medicine Research, Children's National Hospital, Washington DC, USA.
Dingyu Liu, Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
Weiwei Dai, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Natalie McMyn, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Qingyang Wang, Department of Statistics and Data Science, University of California, Los Angeles, CA, USA.
Dapeng Yang, Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
Adam Krejci, Myllia Biotechnology GmbH. Am Kanal 27, 1110 Vienna Austria.
Anatoly Vasilyev, Myllia Biotechnology GmbH. Am Kanal 27, 1110 Vienna Austria.
Nicole Untermoser, Myllia Biotechnology GmbH. Am Kanal 27, 1110 Vienna Austria.
Anke Loregger, Myllia Biotechnology GmbH. Am Kanal 27, 1110 Vienna Austria.
Dongyuan Song, Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA.
Breanna Williams, Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
Bess Rosen, Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
Xiaolong Cheng, Center for Genetic Medicine Research, Children's National Hospital, Washington DC, USA.
Lumen Chao, Center for Genetic Medicine Research, Children's National Hospital, Washington DC, USA.
Hanuman T. Kale, Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
Hao Zhang, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Yarui Diao, Department of Cell Biology, Duke University Medical Center, Durham, NC, USA.
Tilmann Bürckstümmer, Myllia Biotechnology GmbH. Am Kanal 27, 1110 Vienna Austria.
Jenet M. Siliciano, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jingyi Jessica Li, Department of Statistics and Data Science, University of California, Los Angeles, CA, USA.
Robert Siliciano, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Danwei Huangfu, Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
Wei Li, Center for Genetic Medicine Research, Children's National Hospital, Washington DC, USA.

Document Type

Journal Article

Publication Date

11-29-2023

Journal

bioRxiv : the preprint server for biology

DOI

10.1101/2023.10.30.564796

Keywords

CRISPR-based genetic perturbations; Perturb-seq; computational model; single-cell RNA-seq

Abstract

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets.

Department

Genomics and Precision Medicine

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