LCL: Contrastive Learning for Lineage Barcoded scRNA-seq Data

Single-cell lineage tracing technology has advanced the investigation of progenitor cells’ development using static, inheritable barcodes. It can determine temporal dynamics in progenitor-progeny relationships through single-cell RNA-sequencing (scRNA-seq) data. However, studying fate commitment from scRNA-seq can be difficult since the gene expression profiles are confounded with information about many cell processes beyond fate commitment. This paper demonstrates a novel framework to specifically isolate lineage signals driving cell fate, allowing us to learn the gene pathways that differentiate different lineages based on their eventual fates.

Our novel approach, LCL (Lineage-aware Contrastive Learning), is a contrastive-learning deep learning model for analyzing lineage-tracing scRNA-seq data. Using two lineage-tracing datasets, one about reprogramming embryonic fibroblasts and the other about hematopoietic progenitor cells, we demonstrate that LCL can produce low-dimensional representations that effectively isolate fate-determining signals from other key biological signals. We evaluate the quality of LCL embeddings and demonstrate that they perform well in out-of-sample evaluation, both in terms of predicting the lineage and cell type compositions at a future time point. LCL also enables us to identify differential genes stably expressed within a lineage and visualize the fate-determining landscape using self-organizing maps based on the results from LCL. Lastly, we demonstrate the consistency of our approach across datasets of varying complexity using a series of pseudo-real datasets. In conclusion, our results demonstrate that LCL allows researchers to explore fate commitment in single-cell lineage-tracing data and uncover lineage-specific gene pathways.