Python notebooks containing analysis of clonal gene expression, with a bit of proteomics and ATAC for good measure. This repository has directories titled Figures 1 through 5, mirroring our paper Stable clonal gene expression adds a layer of diversity within cell types (to be released in early 2022). This repository was created and is maintained by Marty Weissman, and he takes responsibility/blame for its contents. The other authors of the paper are Jeff Mold, Michael Ratz, Michael Hagemann-Jensen, Joanna Hård, Carl-Johan Eriksson, Hossein Toosi, Joseph Berghenståhle, Kim Blom, Jens Lagergren, Joakim Lundeberg, Jakob Michaelsson, and Jonas Frisén.

Each Figure directory contains the Python notebook that was used to create the figure from the paper, as well as related supplementary figures and tables. The data directory contains much of the processed data used in the paper, and notebooks used for preprocessing. But large exprsesion matrices (from scRNA and ATACseq) are not here due to size constraints. Please reach out to the authors if you would like those. The data directory contains cell metadata (TCR sequences, clone IDs, donor, day, etc.), and gene data (basic genomic information, clonal variability, etc.) among other things.

Cells of the same type often differ in their expression of mRNAs and proteins. This has been attributed to the stochastic nature of transcription, resulting in fluctuations around the mean over short time scales (1-3). Such transcriptional differences have been implicated in the selection of cells during chemotherapy, where cells of a certain gene expression profile may escape therapy (4,5). Less is known about whether, beneath short-term stochastic fluctuations, there exist stable, heritable differences between cells of the same type. To study this, we tracked clonally related T cells based on shared T cell receptor rearrangements to study genome-wide transcriptome and chromatin accessibility profiles of cells descended from individual naïve CD8+ T cells in response to an antigen in humans. Using this strategy, we found that clonal transcriptional profiles are stable over years in vivo in humans and affect thousands of expressed genes. Clone-specific gene expression profiles were retained during reactivation and differentiation of clonally related cells in separate environments. We provide evidence that clonal gene expression mirrors heritable chromatin accessibility, indicating that stable epigenetic states dictate clonal gene expression profiles. Clonal gene expression is not specific to T cells, or humans, as we also find evidence of this in genetically barcoded cells in the mouse brain. Long term clonal gene expression profiles add a layer of diversity within cell types, which may influence developmental cell selection, plasticity and response to therapies.

1. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183-1186, doi:10.1126/science.1070919 (2002).
2. Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544-547, doi:10.1038/nature06965 (2008).
3. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216-226, doi:10.1016/j.cell.2008.09.050 (2008).
4. Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431-435, doi:10.1038/nature22794 (2017).
5. Emert, B. L. et al. Variability within rare cell states enables multiple paths toward drug resistance. Nat Biotechnol 39, 865-876, doi:10.1038/s41587-021-00837-3 (2021).