Investigating the origins of cell heterogeneity via lineage tracing and single cell profiling of expression and chromatin accessibility Anna Minkina, Junyue Cao, Jay Shendure. Presenter affiliation: University of Washington, Seattle, Washington. Expression heterogeneity between ancestrally-related cells underpins cell fate divergence in development and disease. Differential expression states that are transmitted to daughter cells are potentially caused by genetic changes, stably-inherited epigenetic states, or both. Additionally, some expression heterogeneity arises from experimental noise, stochasticity, and other factors. Distinguishing between stochastic and heritable variation is possible if lineage relationships between single cells are known, as heritable expression states should be shared by closely related cells. Here, we present a method to record and capture lineage relationships alongside single cell transcriptomes (sci-RNA-seq) and single cell chromatin accessibility (sci- ATAC-seq) profiles. Using this method, we describe how a single cell grown in culture yields a transcriptionally-diverse cell population. By aggregating single cell profiles of closely related cells, we are able to detect small, heritable expression differences between cell groups which would be overshadowed by stochastic variation were lineage relationships not known. Shared lineage profiles also provide a link between transcription and accessibility states of closely related cell groups, enabling us to associate transcriptional variation with changes to chromatin state at or near the same genes. We observe both large-scale karyotypic instability and more localized expression variation which may arise from local amplifications/deletions and/or regulatory changes. We attempt to distinguish between these possibilities by evaluating relative allele-specific contributions across lineage groups. Because lineage information is recorded progressively over many cell divisions, we can deduce the order of divergence events and thus infer potential causative relationships whereby misregulation at one locus precipitates changes at another. Cumulative genetic instability is a hallmark of cancer, and rare, heritable events not easily detected by single cell profiling likely underpin acquired drug resistance. The strategy presented here offers a promising avenue by which to investigate how there rare expression states emerge and are maintained.