@arcinstitute: PerturbSpace presses a tissue section onto a chip of barcoded microwells. Antibodies in each well tag the cells above w…
Summary
PerturbSpace is a spatial transcriptomics method that presses a tissue section onto a chip of barcoded microwells, using antibodies to tag cells with location codes before single-cell sequencing, achieving >90% confident spatial assignment.
View Cached Full Text
Cached at: 05/27/26, 05:18 AM
PerturbSpace presses a tissue section onto a chip of barcoded microwells. Antibodies in each well tag the cells above with that well’s location code. The tagged cells then go through standard single-cell sequencing, with >90% getting a confident spatial assignment. https://t.co/nF8PaJiHL0
Similar Articles
@arcinstitute: Because PerturbSpace uses standard single-cell sequencing, it's compatible with any single-cell readout. In one day, th…
Arc Institute's PerturbSpace enables high-throughput single-cell profiling of transcriptome, location, CRISPR guides, clonal relationships, and surface proteins from many samples in one day, using standard single-cell sequencing.
@arcinstitute: Most spatial CRISPR screens require trade-offs in throughput or readout depth. A new preprint from @alexnevue, @Inna_Av…
A new preprint from the Arc Institute introduces PerturbSpace, a method for spatially resolved, multimodal whole-transcriptome CRISPR screens compatible with standard single-cell workflows.
SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
SpaFactor is a lightweight framework that predicts spatial transcriptomics from histology images by integrating spatial context and gene programs, demonstrating improved accuracy and biological fidelity across multiple cohorts.
CellWorld: From Gene-Level Reconstruction to Latent Cell Prediction in Spatial Transcriptomics Foundation Models
CellWorld introduces a latent-space predictive pretraining approach for spatial transcriptomics foundation models, predicting latent representations of masked cells instead of reconstructing gene measurements. Across held-out datasets, even small variants outperform existing baselines on all benchmarks, showing that scaling and broad biological diversity improve transferability.
@arcinstitute: Science Fellow @zhou_jingtian builds single-cell and computational frameworks to probe the 3D genome. His work tells us…
Jingtian Zhou builds single-cell and computational frameworks to probe the 3D genome, contributing to major publications in Science, Cell, and Cancer Cell that advance understanding of cell states and diseases.