Tumour heterogeneity plays a crucial role in cancer progression that remains incompletely understood. Therapeutic resistance develops from sub-clonal populations within individual tumours that are frequently masked by sequencing of bulk tumour populations. Their rarity, coupled with the complex interplay of genetic and epigenetic factors driving their behaviour, makes studying the functional roles of these malignant subpopulations exceptionally difficult. Understanding how these clones emerge, survive, and contribute to disease is essential for advancing our understanding of tumour biology, but current tools lack the sensitivity to isolate and analyse them comprehensively.
To address these limitations, we previously developed Single-Cell Profiling and Lineage Tracing (SPLINTR), a technology that maps lineage relationships to transcriptional profiles at single-cell resolution. SPLINTR comprises a powerful tool for the identification and profiling of clonal populations. However, it doesn't allow for retrospective isolation of these clones for further molecular analysis. Moreover, SPLINTR captures only transcriptional data, missing crucial epigenetic information.
Here, we present SPLINTR-CELL, an enhanced platform that expands upon SPLINTR by integrating programmable clone retrieval using CRISPR-activation and the capability for additional multimodal readouts. These improvements enable the isolation of sub-clonal populations and the mapping of their transcriptomic and epigenomic landscapes permitting the detailed molecular characterisation of individual clonal phenotypes across the full time-course of diagnosis, therapy and relapsed disease.
We optimised the SPLINTR-CELL reporter system through spike-in experiments to determine the sensitivity and specificity of cell isolation. We validated the system in vitro and in vivo by isolating and characterising the molecular properties of tumour-initiating clones in different models of acute myeloid leukaemia (AML).
SPLINTR-CELL demonstrated over 80% barcode capture efficiency and could isolate clones as rare as 0.01% within populations. With SPLINTR-CELL, we can now fully characterise the genomic, transcriptomic, and epigenomic features of rare clones, unlocking new insights into their roles in tumorigenesis and relapse.