Advancing and validating single-cell long-read sequencing for broad adoption in cancer molecular profiling

The National Cancer Institute awarded The Jackson Laboratory $452,458 on August 1, 2026, under the Cancer Biology Research program (CFDA 93.396) to advance and validate single-cell long-read sequencing for cancer molecular profiling.

The project develops and validates enhanced tools for single-cell long-read isoform transcriptomics (SCLR-SEQ) to map and analyze full-length spliced isoforms at single-cell resolution in tumor samples. The work addresses technical gaps in current approaches, including suboptimal read length and depth and the absence of customized bioinformatics pipelines for integrating single-cell and long-read sequencing data. The team will rigorously validate the advanced platform using orthogonal methods including fluorescent in situ hybridization and comparison to bulk short- and long-read data from sorted cells, then demonstrate the platform's robustness in detecting intra-tumor splicing heterogeneity. This capability aims to enable the cancer research field to better understand how splicing dysregulation contributes to tumor heterogeneity and influences drug response and resistance, supporting personalized cancer medicine approaches that extend beyond detecting mutations to address transcriptional and post-transcriptional processes.

Performance occurs in Farmington, Connecticut. The award period runs from August 1, 2026, through July 31, 2029.

Abstract

The future of personalized cancer medicine requires going beyond detecting mutations and copy number changes to also considering downstream transcriptional and post-transcriptional processes that influence cellular cancer phenotypes. Alternative splicing is a key, final step in gene expression regulation prior to translation, dictating cellular function and plasticity through expression of diverse transcript and protein isoforms from a single gene. The genetic and epigenetic insults that accumulate in tumors are upstream of splicing, and therefore therapies based on splicing have the potential to more proximally and specifically influence cancer cell phenotype. Splicing dysregulation is a hallmark of cancer and a robust indicator of cancer risk and has been causally linked to tumorigenesis. However, how splicing contributes to tumor heterogeneity and impacts drug response and resistance remains understudied, which hinders exploiting splicing vulnerabilities for clinical benefit. This knowledge GAP is a result of an important technical GAP: the cancer research field lacks robust, validated tools for mapping and analyzing full-length spliced isoforms at the single-cell resolution in tumors. To meet this need, we will build and validate enhanced tools for single-cell long-read isoform transcriptomics (SCLRSEQ) (aim 1). We will address specific shortcomings of currently emerging sclr-SEQ approaches, including suboptimal read length/depth and the absence of bioinformatics pipelines customized for integrating single-cell and LR-SEQ data. We will rigorously validate our advanced sclr-SEQ platform using orthogonal methods, including fluorescent in situ hybridization and through comparison to bulk short- and long-read data from sorted cells. Next, we will demonstrate the robustness of sclr-SEQ in detecting intra-tumor splicing heterogeneity, and the broad applicability of sclr-SEQ across tumor types (aim 2). We will apply sclr-SEQ in a patient-derived xenograft (PDX) model of breast cancer and cisplatin resistance which contains at least five clonal populations that are heterogeneous in their response to therapy and in spliced isoform content. We will use a PDX model because cancer arises within, and is shaped by, the whole organism; tumor progression and treatment response are systemic processes that in vitro systems cannot fully replicate. Thus, in vivo models are required for comprehensive validation of our platform. To demonstrate effectiveness of sclr-SEQ across tumor types, we will use patient's lung and malignant peripheral sheath nerve tumors, selected for their divergent cell type lineages and compositions. We will define the splicing landscape in cancer cells, tumor-resident immune cells, cancerassociated fibroblasts, and circulating blood cells. Finally, we will develop open-access, web-based data sharing and visualization tools for single cell isoform expression (aim 3). By bringing sclr-SEQ and its associated computational methods to technical and analytical maturity, we will encourage and facilitate broad adoption by the cancer research community. This project will accelerate translation of splicing biology into the clinic, advancing precision oncology through enabling new strategies for early detection, intervention, and prevention.