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Reading the genes of each individual cell, and mapping where each cell sits in the tumour.
scRNA-seq (10x Genomics) resolves tumour, immune, and stromal populations; spatial transcriptomics (Visium, Xenium, CosMx, MERFISH) and multiplex protein imaging (CODEX, IMC) keep tissue architecture. Revealing how ADC bystander killing, T-cell exclusion, and resistance niches work. Research-grade; entering trials as correlative science.
RNA is captured with barcodes per cell or per spatial location, or read by imaging-based in situ hybridisation for hundreds to thousands of genes.
Nothing in the corpus depends on this yet.
Dependencies are what this technology cannot be delivered without: manufacturing steps, instruments, software, upstream methods. See its full chain on the map.
It is the strongest case that mutation burden is real biology in lung cancer and, at the same time, the clearest demonstration that its threshold is not fixed, which is why it never became a reliable selector.
It explains why one biopsy can mislead and why chemotherapy selects for the protective neighbourhood, an argument for spatial rather than bulk profiling.
It explains why single-sample classifiers disagree on about one tumour in eight and why KRAS allelic imbalance and GATA6 copy number are being read alongside expression.
Fibroblasts are part of the immune conversation, not just a physical barrier, which is why stromal and immune strategies are now designed together.
It sets the baseline the adenoma-carcinoma sequence starts from and warns against reading a driver mutation found in tissue, or in stool or blood, as evidence of cancer.
Query for this technology: (TITLE:"single-cell RNA sequencing" OR ABSTRACT:"single-cell RNA sequencing" OR TITLE:"spatial transcriptomics" OR ABSTRACT:"spatial transcriptomics" OR TITLE:"single-cell" OR ABSTRACT:"single-cell") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Single-cell & spatial profiling, not a curated reading list.
Shares Miriam Merad, Map which tumour clones sit next to which immune cells before choosing therapy, Standards for spatial and multiplex tissue biomarkers before they reach the clinic, Clear the suppressive neutrophils out of pancreatic tumours first.
Shares Clear the suppressive neutrophils out of pancreatic tumours first, Reprogramme suppressive macrophages instead of trying to delete them, Grow immune command posts inside tumours, Implant a tiny device that tests twenty drugs inside the patient's own tumour.
Shares Block the survival signals the tumour's neighbours provide, Targeting tumour mechanics and pressure, Match therapy to the type of scar-forming cell in the tumour, What actually holds T cells at the tumour border?.
Shares Targeting tumour mechanics and pressure, Clear the suppressive neutrophils out of pancreatic tumours first, Spatially confined sub-tumor microenvironments in pancreatic cancer, Fibroblast subtypes in the pancreatic cancer stroma (myCAF, iCAF and apCAF).
Shares Map which tumour clones sit next to which immune cells before choosing therapy, Standards for spatial and multiplex tissue biomarkers before they reach the clinic, Grow immune command posts inside tumours, Spatial-omics-guided treatment selection.
Shares Map which tumour clones sit next to which immune cells before choosing therapy, Standards for spatial and multiplex tissue biomarkers before they reach the clinic, Clear the suppressive neutrophils out of pancreatic tumours first, Reprogramme suppressive macrophages instead of trying to delete them.
Shares Nucleai, Map which tumour clones sit next to which immune cells before choosing therapy, Standards for spatial and multiplex tissue biomarkers before they reach the clinic, Clear the suppressive neutrophils out of pancreatic tumours first.
Shares Elucidate Bio, Find the parts of a tumour the drug never reaches, A global rapid tissue donation network for metastatic disease, Spatial-omics-guided treatment selection.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
The R toolkit for single-cell genomics from the Satija lab, with integration, clustering and spatial support.
The Python toolkit for single-cell gene expression analysis, the centre of the scverse ecosystem used across tumour single-cell studies.
A generalist deep-learning cell segmentation model used widely on histology, multiplex and cell-culture images.
Deep probabilistic models for single-cell omics: integration, annotation and differential expression.
A generative pretrained transformer for single-cell biology with released weights, from the Bo Wang lab.
The Chan Zuckerberg Initiative's interactive explorer for single-cell datasets, behind the CELLxGENE Discover portal.
The annotated data matrix format that scanpy, cellxgene and most single-cell tools share.
Infers large-scale copy-number variation from single-cell RNA-seq to tell tumour cells from normal, from the Trinity CTAT project.
Open-source software, hardware and data projects catalogued by a third party, the Open Medical Registry, that bear on this technology. Listing is not endorsement; check each project's own licence and validation before clinical use.
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images.
An end-to-end processing pipeline that transforms multi-channel whole-slide images into single-cell data.
An open, collaborative project to analyze data from the Single-cell Pediatric Cancer Atlas (ScPCA) Portal
R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells.
From the Open Medical Registry (openmedical.sh), an MIT-licensed catalogue of open-source medicine. Blurbs are one line from each registry record; every project keeps its own licence.