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Vander Velde, R. J.

Publications and source records attributed to Vander Velde, R. J..

4 recordsLinked to original sources

Multiplexed enrichment and tracking of lineages with CloneSweeper

A fundamental challenge in studying therapy resistance is understanding whether it results from pre-existing cellular states ("priming") or drug-induced changes ("adaptation"). While lineage barcoding enables retrospective analysis of cells before and after treatment, current methods struggle to efficiently capture rare lineages in single-cell RNA sequencing (scRNA-seq) or isolate multiple specific lineages simultaneously for functional study. To overcome these limitations, we developed CloneSweeper, a multiplexed lineage tracking platform that pools enrichment libraries to isolate or enrich multiple rare lineages. CloneSweeper utilizes a dual-function barcode expressed as both a Cas9 gRNA for live-cell sorting and a 3 UTR transcript for high-recovery detection in 10x Genomics scRNA-seq. We applied CloneSweeper to a model of BRAF V600E melanoma, where we identified that resistance to targeted therapy emerges from a polyclonal population of rare, pre-existing lineages. By simultaneously targeting and enriching 21 distinct rare lineages prior to treatment, we defined a heritable, primed state characterized by de-differentiation and elevated mesenchymal markers. We demonstrate that these primed cells are not quiescent but instead exhibit upregulated inflammatory and stress response signaling, specifically via the AP-1 and NF-{kappa}B1 pathways. CloneSweeper thus provides a robust framework for dissecting the molecular mechanisms of rare biological phenomena through simultaneous, multiplexed lineage isolation.

bioengineering↗

SpaceBar enables clone tracing in spatial transcriptomic data

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

genomics↗

Clonal differences underlie variable responses to sequential and prolonged treatment

Cancer cells exhibit dramatic differences in gene expression at the single-cell level which can predict whether they become resistant to treatment. Treatment perpetuates this heterogeneity, resulting in a diversity of cell states among resistant clones. However, it remains unclear whether these differences lead to distinct responses when another treatment is applied or the same treatment is continued. In this study, we combined single-cell RNA-sequencing with barcoding to track resistant clones through prolonged and sequential treatments. We found that cells within the same clone have similar gene expression states after multiple rounds of treatment. Moreover, we demonstrated that individual clones have distinct and differing fates, including growth, survival, or death, when subjected to a second treatment or when the first treatment is continued. By identifying gene expression states that predict clone survival, this work provides a foundation for selecting optimal therapies that target the most aggressive resistant clones within a tumor.

systems biology↗

The impact of tumor stromal architecture on therapy response and clinical progression

-- Advances in molecular oncology research culminated in the development of targeted therapies that act on defined molecular targets either on tumor cells directly (such as inhibitors of oncogenic kinases), or indirectly by targeting the tumor microenvironment (such as anti-angiogenesis drugs). These therapies can induce strong clinical responses, when properly matched to patients. Unfortunately, most targeted therapies ultimately fail as tumors evolve resistance. Tumors consist not only of neoplastic cells, but also of stroma, whereby \"stroma\" is the umbrella term for non-tumor cells and extracellular matrix (ECM) within the tumor microenvironment, possibly excluding immune cells1. We know that tumor stroma is an important player in the development of resistance. We also know that stromal architecture is spatially complex, differs from patient to patient and changes with therapy. However, to this date we do not understand the link between spatial and temporal changes in stromal architecture and response of tumors to therapy, in space and time. In this project we sought to address this gap of knowledge using a combination of mathematical and statistical modeling, experimental in vivo studies, and analysis of clinical samples in therapies that target tumor cells directly (in lung and breast cancers) and indirectly (in kidney cancer). This knowledge will inform therapy choices and offer new angles for therapeutic interventions. Our main question is: how does spatial architecture of stroma impact the emergence or evolution of resistance to targeted therapies, and how can we use this knowledge clinically?

cancer biology↗