Tethering distinct molecular profiles of single cells by their lineage histories to investigate sources of cell state heterogeneity
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Evaluation Summary:
The authors use single-cell RNA-sequencing, single-cell ATAC-sequencing, a CRISPR-based lineage tracing system, and a novel computational pipeline to characterize heritable expression changes. Aspects of this work were found to be both impactful and technically sound, but there is a concern with the scalability/generalizability of the approach, the use of the single cell ATAC-sequencing data, and some technical aspects of the computational pipeline. This work will appeal to groups working on lineage tracing and gene regulation.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)
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Abstract
Gene expression heterogeneity is ubiquitous within single cell datasets, even among cells of the same type. Heritable expression differences, defined here as those which persist over multiple cell divisions, are of particular interest, as they can underlie processes including cell differentiation during development as well as the clonal selection of drug-resistant cancer cells. However, heritable sources of variation are difficult to disentangle from non-heritable ones, such as cell cycle stage, asynchronous transcription, and measurement noise. Since heritable states should be shared by lineally related cells, we sought to leverage CRISPR-based lineage tracing, together with single cell molecular profiling, to discriminate between heritable and non-heritable variation in gene expression. We show that high efficiency capture of lineage profiles alongside single cell gene expression enables accurate lineage tree reconstruction and reveals an abundance of progressive, heritable gene expression changes. We find that a subset of these are likely mediated by structural genetic variation (copy number alterations, translocations), but that the stable attributes of others cannot be understood with expression data alone. Towards addressing this, we develop a method to capture cell lineage histories alongside single cell chromatin accessibility profiles, such that expression and chromatin accessibility of closely related cells can be linked via their lineage histories. We call this indirect “coassay” approach “THE LORAX” and leverage it to explore the genetic and epigenetic mechanisms underlying heritable gene expression changes. Using this approach, we show that we can discern between heritable gene expression differences mediated by large and small copy number changes, trans effects, and possible epigenetic variation.
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Evaluation Summary:
The authors use single-cell RNA-sequencing, single-cell ATAC-sequencing, a CRISPR-based lineage tracing system, and a novel computational pipeline to characterize heritable expression changes. Aspects of this work were found to be both impactful and technically sound, but there is a concern with the scalability/generalizability of the approach, the use of the single cell ATAC-sequencing data, and some technical aspects of the computational pipeline. This work will appeal to groups working on lineage tracing and gene regulation.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
Here, Minkina et al., present 'The LORAX', a CRISPR-based single-cell lineage tracing to understand heritable and non-heritable variation in gene expression. To this end, they have adapted sci-RNAseq, a plate-based single-cell RNA sequencing method, to capture lineage barcodes. Additionally, they have developed a novel computational pipeline to construct lineage trees from the resulting data to identify differentially enriched genes between distinct branches of their lineage tree. To explain heritable variation in gene expression, they use allelic ratios of transcripts to infer large copy number alterations along branches of their lineage tree. Finally, to understand non-CNA-based reasons for variation in clonal gene expression, they adapted sci-ATACseq, a plate-based single-cell ATAC sequencing assay, to …
Reviewer #1 (Public Review):
Here, Minkina et al., present 'The LORAX', a CRISPR-based single-cell lineage tracing to understand heritable and non-heritable variation in gene expression. To this end, they have adapted sci-RNAseq, a plate-based single-cell RNA sequencing method, to capture lineage barcodes. Additionally, they have developed a novel computational pipeline to construct lineage trees from the resulting data to identify differentially enriched genes between distinct branches of their lineage tree. To explain heritable variation in gene expression, they use allelic ratios of transcripts to infer large copy number alterations along branches of their lineage tree. Finally, to understand non-CNA-based reasons for variation in clonal gene expression, they adapted sci-ATACseq, a plate-based single-cell ATAC sequencing assay, to capture CRISPR lineage barcodes. Using an existing lineage tree derived from sci-RNAseq, they are able to assign individual sci-ATACseq cells with lineage information to distinct branches of the lineage tree and associate gene expression and chromatin accessibility landscape within sub-clones. They use this information to rule out CNAs as a source of clonal variation in gene expression for some of their candidate genes.
This manuscript aims to study the heritability of cell state, an exciting field re-invigorated by recent technical advances in single-cell lineage tracing. However, enthusiasm for the approach is tempered for several reasons. First, the authors apply their lineage tracing method to a clonal population of HEK293T cells, an immortalized cell line. Thus, it is currently unclear what the broader biological significance will be, and whether this approach can be readily deployed in other systems. Second, the authors propose a new computational pipeline for constructing lineage trees but fail to fully benchmark its accuracy using ground truth data. Third, while the authors argue that lineage resolved chromatin accessibility landscapes could help explain some of the heritable gene expression patterns observed in their data, they do not convincingly demonstrate this with their data.
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Reviewer #2 (Public Review):
In this paper, Minkina and colleagues describe an experimental and computational technique for studying heritable sources of gene expression heterogeneity and for further quantifying the extent to which they are attributed to copy number alterations. They achieve this through the development and validation of a dynamic CRISPR barcoding approach that is compatible with single-cell RNA and ATAC sequencing (with sci-RNA-seq and sci-ATAC-seq).
Overall, the method is technically very solid, and the lineage reconstruction from the CRISPR edited barcodes is concordant with lineage structure revealed by copy number alterations.
Ultimately, they do find some genes that show heritable expression without associated copy number changes. Interestingly, they do not find an associated change in the chromatin by ATAC-seq.
On…
Reviewer #2 (Public Review):
In this paper, Minkina and colleagues describe an experimental and computational technique for studying heritable sources of gene expression heterogeneity and for further quantifying the extent to which they are attributed to copy number alterations. They achieve this through the development and validation of a dynamic CRISPR barcoding approach that is compatible with single-cell RNA and ATAC sequencing (with sci-RNA-seq and sci-ATAC-seq).
Overall, the method is technically very solid, and the lineage reconstruction from the CRISPR edited barcodes is concordant with lineage structure revealed by copy number alterations.
Ultimately, they do find some genes that show heritable expression without associated copy number changes. Interestingly, they do not find an associated change in the chromatin by ATAC-seq.
One of the strengths of this paper is the discussion of the technical limitations of the technique. They highlight both strengths and weaknesses of the approach such that the reader can grasp each (both throughout the text and extensively in the discussion).
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