Karolinska University Hospital

3 related articles

Subscribe to updates

Nature Methods Oct 23, 2026
Cell segmentation in spatial transcriptomics gets a cross-platform test: SAM tops the raw score, SCS leads once coverage counts
Cell segmentation in spatial transcriptomics has lacked a cross-platform comparison with biologically meaningful metrics. A benchmark evaluates 11 image-based, transcriptomics-based and multimodal methods across 15 datasets from 5 technologies and about 2.3 million cells, scoring segmentation quality, transcript recovery, morphology, expression purity and spatial organization. On the average of 15 metrics, SAM scores highest (0.58), ahead of SAM2 (0.53), BIDCell (0.52) and SCS (0.48); after adjusting by the ratio of segmented to reference cells, SCS leads (0.34), with BIDCell and Baysor both at 0.33.
Cell segmentation in spatial transcriptomics has lacked a cross-platform comparison with biologically meaningful metrics. A benchmark evaluates 11 image-based, transcriptomics-based and multimodal methods across 15 datasets from 5 technologies and about 2.3 million cells, scoring segmentation quality, transcript recovery, morphology, expression purity and spatial organization. On the average of 15 metrics, SAM scores highest (0.58), ahead of SAM2 (0.53), BIDCell (0.52) and SCS (0.48); after adjusting by the ratio of segmented to reference cells, SCS leads (0.34), with BIDCell and Baysor both at 0.33.
Karolinska University Hospital King Abdullah University of Science and Technology Ilia State University Karolinska Institutet
Xabier Martinez‐de‐Morentin（First author, Corresponding author）Jesper N. Tegner（Corresponding author）David Gómez-Cabrero（Corresponding author）
Cell segmentation Spatial transcriptomics CellSPA Segment Anything Model
Nature Machine Intelligence Oct 19, 2026
Joint multi-sample network inference steadies omics reconstructions, but a prior cut to 20% leaves zero active reactions
Multi-sample regulatory network inference has long solved each sample separately and stitched shared structure from overlaps, which is unstable under noise. CORNETO instead solves several samples inside one mixed-integer constrained optimization with structured sparsity penalties. On haematopoietic and adult cortical single-cell data, joint inference generally improved robustness and reproducibility, while predictive gains depended on biological context and prior-network completeness.
Multi-sample regulatory network inference has long solved each sample separately and stitched shared structure from overlaps, which is unstable under noise. CORNETO instead solves several samples inside one mixed-integer constrained optimization with structured sparsity penalties. On haematopoietic and adult cortical single-cell data, joint inference generally improved robustness and reproducibility, while predictive gains depended on biological context and prior-network completeness.
Karolinska University Hospital King Abdullah University of Science and Technology Ilia State University Karolinska Institutet
Xabier Martinez‐de‐Morentin（First author, Corresponding author）David Gómez-Cabrero（Corresponding author）
CORNETO Multi-sample constrained network inference Bootstrap aggregation Selective re-expansion
Frontiers in Pharmacology Sep 25, 2026
Review proposes a microbiome-aware oral formulation framework: excipient risk classification, nanocarrier design rules, and a tiered testing roadmap
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
This review integrates evidence on microbial drug metabolism, excipient–microbiome interactions, nanocarrier–microbiome interfaces, microbiota-responsive release mechanisms, and experimental models to propose an authors' evidence-informed framework for oral formulation design, comprising an excipient–microbiome risk classification, nanocarrier design rules, microbiota-responsive delivery decision logic, and a tiered testing roadmap, illustrated by clinically relevant examples such as digoxin inactivation by Eggerthella lenta, bacterial levodopa metabolism, and microbial beta-glucuronidase-mediated irinotecan toxicity.
Karolinska University Hospital Egypt-Japan University of Science and Technology Cairo University Karolinska Institutet
mohamed sedeek（First author）Sadek Ahmed（Corresponding author）Osama Saher（Corresponding author）
Microbiome-Aware Oral Drug Delivery Excipient–Microbiome Interactions Microbial Drug Metabolism Controlled drug release

You’re all caught up

Cell segmentation in spatial transcriptomics gets a cross-platform test: SAM tops the raw score, SCS leads once coverage counts

Joint multi-sample network inference steadies omics reconstructions, but a prior cut to 20% leaves zero active reactions

Review proposes a microbiome-aware oral formulation framework: excipient risk classification, nanocarrier design rules, and a tiered testing roadmap