Weekly reads 1/06/26

Spatial screens, communication maps, and a pancreatic cancer milestone

This week’s reads showcases how rapidly spatial biology is evolving, from new ways to quantify tissue architecture and cell-cell communication to technologies that bring perturbation screening into spatial context. Several studies go past the traditional neighborhood-focused approach by suggesting novel frameworks, including those called COSTE and CellSTIC, that incorporate multiscale structure and hierarchical communication programs within the tissue context. Novel technologies, represented by PerturbSpace, show how gene perturbation can be connected directly with the phenotypic spatial tissue properties, and GEARS addresses one of the long-standing problems of the field by reconstructing the tissue structure using single-cell information alone. Beyond technology, one of this week’s papers also sheds light on the significance of tissue structure from a biological perspective, since different cancer-associated fibroblast subtypes seem to play an important role in organizing either permissive or immunosuppressive tumor environments. Finally, a ground-breaking phase 3 clinical investigation for pancreatic cancer describes a novel success of daraxonrasib, which is a RAS(ON) inhibitor.

Preprints/articles that I managed to read this week

Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics

Long et al. bioRxiv (2026). 10.64898/2026.05.26.727847

The paper in one sentence

COSTE uses directed nearest-neighbor distance profiles and hierarchical clustering to generate a sample-normalized Spatial Separation Score (SSS) that quantifies multiscale spatial relationships between cell types or transcripts without requiring a predefined neighborhood radius.

Summary

Spatial omics technologies generate rich maps of cell types and transcripts in tissues, but most analysis tools focus on local neighborhoods (e.g., cells within a fixed radius or k-nearest neighbors), which can miss longer-range and hierarchical tissue architecture. The authors introduce COSTE, a framework that computes directed inter-type distances: for each “searcher” cell type, the average distance to the nearest cell of every other “findee” type is calculated, producing an asymmetric distance matrix. Hierarchical clustering of these profiles yields a dendrogram, and cophenetic distances are normalized within each sample to produce a Spatial Separation Score (SSS) from 0 to 1 (lower = more similar spatial profiles). COSTE is segmentation‑free and can be applied to both cell-type labels and individual transcripts. Benchmarking on synthetic modular and nested patterns shows that COSTE captures nested hierarchical geometries more directly than local neighborhood enrichment methods (Squidpy, Giotto, ANE), which require parameter tuning and do not consistently recover layered structures. On a neonatal mouse pup Xenium dataset (~1.3 million cells, 44 types), COSTE reveals a structure‑within‑structure hierarchy (e.g., retina layers forming a tight cluster; two fibroblast subtypes segregating into distinct spatial domains). In a pulmonary fibrosis cohort, SSS between AT2 and capillary cells (defined as a TRU Remodeling Score) increases with disease severity, reflecting progressive alveolar‑capillary uncoupling. COSTE also recapitulates known lymph node compartments (B cell cortex, T cell paracortex, medulla). Transcript‑level analysis in a systemic sclerosis lung sample identifies a pleural‑enriched gene module (including IL10, CCL4, WT1) without cell segmentation. Finally, on TNBC imaging mass cytometry data, COSTE associates response to chemo‑immunotherapy with spatial separation patterns between antigen‑presenting cells and CD8+ T cells. The method is computationally efficient (avoids permutations) and scales to million‑cell datasets.

Personal highlights

1. Hierarchical spatial representation without predefined radius: COSTE builds a dendrogram from directed nearest‑neighbor distance profiles, capturing both local proximity and longer‑range nesting (e.g., cell types that form concentric layers). This contrasts with local neighborhood enrichment methods that require a user‑set radius or k and often miss nested geometry.

2. Segmentation‑free and transcript‑level analysis: COSTE can operate directly on single‑transcript coordinates, enabling spatial gene module discovery without cell segmentation. Applied to a fibrotic lung sample, it nominated a pleural‑enriched module containing chemokines (CCL4, IL10) and mesothelial marker WT1, all co‑localized in a thickened pleural region.

3. Quantitative architectural remodeling score (SSS): The sample‑normalized Spatial Separation Score provides a relative metric of spatial coupling. In pulmonary fibrosis, the SSS between AT2 and capillary cells (TRU Remodeling Score) shows a monotonic increase from healthy to severely affected lungs, and correlates with histological features (remnant alveoli vs. honeycombing).

4. Cross‑platform and cross‑scale utility: Validated on Xenium (mouse pup, human lung fibrosis, lymph node) and imaging mass cytometry (TNBC cohort), COSTE summarizes spatial organization from cell‑type communities down to individual transcript co‑localization.

5. Computational efficiency: Avoids costly permutation tests used by Squidpy and Giotto. On the neonatal mouse pup dataset (~1.3M cells), COSTE runs orders of magnitude faster with lower peak memory than permutation‑based methods under the tested configurations.

Why should we care?

The main takeaway is that COSTE offers a complementary lens to local neighborhood analysis, one that emphasizes global, multiscale arrangements. It is a useful hypothesis‑generating tool for spatial transcriptomics, especially when tissue architecture is suspected to be layered or nested. But it is not a replacement for traditional methods, and any biological interpretations derived from SSS should be validated with orthogonal approaches (e.g., spatial mapping of key cell‑type pairs, functional perturbation). Users should be cautious about over‑interpreting cross‑sample comparisons and should always inspect the raw spatial data to confirm that low SSS truly reflects biological co‑localization rather than density artifacts.

Decoding Hierarchical Cell-Cell Communication in Spatial Multi-Omics with CellSTIC

Wang et al. bioRxiv (2026). 10.64898/2026.05.27.728114

The paper in one sentence

CellSTIC is a deep learning framework that integrates spatial multi-omics data (RNA, protein, chromatin) with a hierarchical semantic tree of ligand-receptor interactions to infer cell-cell communication as structured, multiscale programs that are traceable from broad functional modules down to individual molecular pairs.

Summary

Cell-cell communication (CCC) is typically inferred from spatial transcriptomics as lists of ligand-receptor (LR) pairs, which are difficult to interpret and compare across tissues or conditions. CellSTIC addresses this by treating CCC as an organized feature of tissue architecture rather than isolated interactions. The framework has four main components: (1) a Multimodal Evidence Graph Constructor that builds spatially constrained communication graphs integrating local neighborhoods and global clustering from multiple modalities (RNA, protein, chromatin accessibility); (2) a Multimodal Evidence Integrator that learns fused latent representations while preserving spatial domain structure; (3) a Ligand-Receptor Semantics Tree Builder that organizes LR pairs into hierarchical functional modules using three optional strategies (balanced, LLM-guided, or biology-informed); and (4) a hierarchical CCC predictor trained with self-supervised edge masking and reconstruction. Benchmarking on simulated multimodal data with ground-truth CCC shows CellSTIC outperforms COMMOT, CellNEST, Scriabin, and DcjComm in edge prediction (AUROC ~0.92 vs baselines <0.85) and spatial domain identification (ARI, NMI scores higher than MEFISTO, MultiVI, PRAGA, SpatialGlue). On a human lymph node dataset, CellSTIC recovers known B cell cortex, T cell paracortex, and medulla regions, and reveals that Helper/Regulatory T cells communicate with different immune compartments via distinct branches of the LR tree (chemokine vs immunomodulatory programs). On a mouse brain 5M dataset, it resolves 15 spatial domains and identifies region-specific signaling axes (CD200-CD200R4, PENK-OPRK1) with cell-type-resolved sender-receiver patterns, including boundary-enriched communication. On mouse embryonic development (E9.5–E16.5), it distinguishes organ-specific communication trajectories (NTS-SORT1 in brain vs F2-F2R in liver) and captures network topology shifts (decreased global efficiency, increased modularity). On axolotl telencephalon development vs regeneration, it shows that WNT7B-FZD5 signaling during regeneration is not a replay of development but a distinct process with rapid redeployment, fluctuating connectivity, and delayed architectural stabilization

Personal highlights

1. Hierarchical LR tree for multiscale communication: Instead of flat LR lists, CellSTIC organizes interactions into a semantic tree (root → pathways → specific pairs) using three construction strategies. This allows communication to be analyzed at coarse (e.g., “chemokine signaling”) and fine (e.g., “CCL19-CCR7”) levels simultaneously, with traceable links between modules and molecular evidence.

2. Multimodal integration with spatial constraints: Integrates RNA, protein (ADT), and chromatin accessibility (ATAC) within a unified graph that encodes spatial distance, directionality, and cross-modality similarities. Outperforms SpatialGlue and other multimodal integration methods in spatial domain recovery (higher ARI, NMI, V-measure) on synthetic and real tissues.

3. State-of-the-art CCC prediction on simulated benchmarks: Achieves AUROC of 0.92 ± 0.03 across eight simulation replicates, compared to next-best baseline (COMMOT) at ~0.85. Improvements are consistent across LR pairs and not driven by a small subset, with reduced cross-replicate variance.

4. Distinguishes development from regeneration in axolotl brain: Shows that WNT7B-FZD5 communication during telencephalon regeneration follows a different trajectory (early peak at R5, fluctuating edge numbers, transient modularity changes) compared to development (monotonic increase, stable modularity after E54), indicating that regeneration is not a simple replay of developmental programs.

5. Reveals branch-restricted communication programs in lymph node: Post hoc attribution shows that Helper/Regulatory T cells use different branches of the LR tree to communicate with B lineage (broad, spanning chemokine and immunomodulatory branches) versus NK/ILC or erythroid/megakaryocyte compartments (narrow, confined to specific submodules), a level of functional specificity not visible from flat LR analysis.

Daraxonrasib or Chemotherapy in Previously Treated Metastatic Pancreatic Cancer

O'Reilly et al. New England Journal of Medicine (2026). 10.1056/NEJMoa2605555

The paper in one sentence

In a phase 3 trial of patients with previously treated metastatic pancreatic cancer, the oral RAS(ON) multiselective inhibitor daraxonrasib more than doubled median overall survival compared to standard chemotherapy (13.2 vs 6.6 months) with a hazard ratio of 0.40.

Summary

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, with most patients presenting with metastatic disease and median overall survival under 1 year. For patients who progress after first-line therapy, second-line chemotherapy options (gemcitabine plus nab-paclitaxel, liposomal irinotecan-based regimens, or FOLFOX) produce response rates below 15% and median progression-free survival of 3–4 months. More than 90% of PDAC tumors harbor oncogenic RAS mutations, predominantly at codon 12 (G12), which drive aberrant signaling through the active GTP-bound state RAS(ON). Daraxonrasib is an oral, potent inhibitor that targets the active state of both mutant and wild-type RAS across KRAS, NRAS, and HRAS, including G12, G13, and Q61 variants. The RASolute 302 trial was an international, open-label, phase 3 trial randomizing 500 patients with previously treated mPDAC 1:1 to daraxonrasib (300 mg once daily) or investigator’s choice of four standard chemotherapy regimens. The dual primary endpoints were overall survival and progression-free survival (by blinded independent central review) in the RAS G12 mutation population (91.8% of patients). Key secondary endpoints included overall survival and progression-free survival in the overall population (which also included patients with G13/Q61 mutations or no RAS mutation identified), objective response, and patient-reported quality of life. In the RAS G12 population, median overall survival was 13.2 months with daraxonrasib vs 6.6 months with chemotherapy (HR 0.40; 95% CI, 0.30–0.54; P<0.001). Median progression-free survival was 7.3 vs 3.5 months (HR 0.45; 95% CI, 0.34–0.59; P<0.001). Objective response rate was 33.2% vs 11.8%. In the overall population, median overall survival was 13.2 vs 6.7 months (HR 0.40; 95% CI, 0.30–0.53), and progression-free survival was 7.2 vs 3.6 months (HR 0.49). Patient-reported outcomes (pain deterioration, global health status deterioration) were significantly delayed with daraxonrasib (hazard ratios ~0.60, P<0.001). Adverse events of grade ≥3 occurred in 61.8% of daraxonrasib patients vs 69.6% of chemotherapy patients. Treatment-related adverse events leading to discontinuation were rare with daraxonrasib (1.2% vs 11.2%). Daraxonrasib-associated toxicities were predominantly low-grade rash (86.3% overall, 13.7% grade ≥3), diarrhea, stomatitis, and nausea, whereas chemotherapy caused more hematologic toxicity and peripheral neuropathy.

Personal highlights

1. Unprecedented survival benefit in second-line pancreatic cancer: Median overall survival of 13.2 months with daraxonrasib vs 6.6 months with chemotherapy (HR 0.40) more than doubles the expected survival in a disease where historical benchmarks in this setting are 6–7 months.

2. High response rate with durable disease control: Objective response rate of 33% (vs 12% with chemotherapy) and median progression-free survival of 7.3 months (vs 3.5 months) indicate meaningful tumor shrinkage and extended time without progression.

3. Favorable tolerability profile enabling longer treatment: Despite median treatment duration of 6.2 months vs ≤3.2 months for chemotherapy, daraxonrasib had lower rates of grade ≥3 adverse events (61.8% vs 69.6%), fewer treatment discontinuations (1.2% vs 11.2%), and fewer dose reductions (36.1% vs 57.5%). Adverse events were primarily manageable rash and gastrointestinal symptoms.

4. Patient-reported benefits in pain and quality of life: Time to deterioration of pain (9.0 vs 3.7 months) and global health status (5.6 vs 2.4 months) both significantly favored daraxonrasib, demonstrating that survival extension is accompanied by preserved or improved symptom control.

5. Consistent benefit across most subgroups: The hazard ratio for overall survival favored daraxonrasib across ECOG performance status, presence of liver metastases, prior chemotherapy type, and RAS mutation subtypes, though the small number of non-G12 RAS patients (8% of the cohort) precludes definitive conclusions in those subgroups.

Why should we care?

The main takeaway is that daraxonrasib offers a genuine leap forward in pancreatic cancer treatment, but with caveats: the benefit appears largest in the predominant RAS G12-mutant population, the drug has a distinct toxicity profile requiring proactive dermatologic and gastrointestinal management, and the open-label design means some optimism bias cannot be excluded. The results are sufficiently robust that daraxonrasib is likely to become a new standard of care, but confirmatory real-world evidence and cost-effectiveness analyses will determine its ultimate role. The trial also validates the broader therapeutic strategy of directly targeting the active (GTP-bound) state of RAS, a molecular target long considered "undruggable", which may have implications for other RAS-driven cancers beyond pancreatic cancer.

Spatially resolved, multimodal in vivo Perturb-seq using antibody-based cell hashing

Nevue et al. bioRxiv (2026). 10.64898/2026.05.25.727765

The paper in one sentence

PerturbSpace combines antibody-based spatial barcoding of tissue sections with single-cell multiomics and CRISPR perturbations to map, at ~80 µm resolution, how genetic perturbations affect tissue architecture and mediate non‑cell‑autonomous effects in vivo.

Summary

In vivo Perturb-seq has enabled large-scale mapping of gene function in physiological contexts, but conventional single-cell readouts require tissue dissociation, destroying spatial information and precluding analysis of how perturbations influence tissue architecture or exert paracrine effects on neighboring cells. The authors develop PerturbSpace, a method that integrates spatial hashing with high‑throughput single‑cell workflows. Tissue sections are placed onto high‑density microwell arrays pre‑filled with oligonucleotide‑conjugated antibodies against ubiquitously expressed surface markers (MHC‑I and CD45). Each well contains a unique spatial barcode that is transferred to cells via antibody binding. After dissociation, spatially barcoded cells are FACS‑enriched and processed through a modified 10x Genomics 3’ workflow to simultaneously recover transcriptomes, spatial coordinates, surface proteomes (CITE‑seq), CRISPR sgRNAs, and expressed clonal barcodes. The authors apply PerturbSpace in two settings. First, to study regenerative hematopoiesis in the spleen, they transplant Cas9+ hematopoietic progenitors transduced with a lentiviral library targeting 40 transcriptional regulators (plus clonal barcodes) into irradiated mice. After 14 days, they profile splenic colony‑forming units (CFU‑S). They identify 19,174 colonies, classify them into eight composition types, and show that perturbations such as Cebpa loss shifts colonies toward erythroid‑only, while Rcor1 or Gltscr1 loss increases colony size without altering proliferation — associated with upregulation of cell‑adhesion programs. Second, in the liver, they overexpress IFNγ (or a control peptide) in transplanted immune cells and measure paracrine effects on neighboring host cells. IFNγ‑expressing neighborhoods show strong upregulation of interferon‑response signatures (e.g., Gbp6, Stat1) and downregulation of TNF signaling in bystander cells. The method is compatible with orthogonal modalities (CITE‑seq, clonal lineage tracing) and achieves >90% spatial mapping efficiency. Resolution is limited to 80 µm (microwell spacing), sufficient for colony and neighborhood analysis but not for single‑cell morphology or immune synapses.

Personal highlights

1. Spatial hashing with universal surface markers: Uses anti‑MHC‑I and anti‑CD45 antibodies conjugated to oligonucleotides, enabling spatial barcoding of all nucleated mammalian cells without requiring cell‑type‑specific labels or genetic reporters.

2. Multimodal compatibility within a standard 10x workflow: Simultaneously recovers transcriptomes, spatial coordinates, surface proteins (119 markers), CRISPR sgRNAs, and clonal barcodes from the same single‑cell suspension.

3. Discovery of perturbation effects on tissue architecture: Identifies that Rcor1 knockout increases monocytic colony size without increasing proliferation, and that this is associated with upregulation of cell‑cell and cell‑matrix adhesion genes.

4. Direct measurement of non‑cell‑autonomous effects: Uses IFNγ overexpression in liver to show that bystander cells in IFNγ‑positive neighborhoods (but not control neighborhoods) upregulate interferon‑response signatures and downregulate TNF signaling, demonstrating PerturbSpace’s ability to map paracrine signaling in situ.

5. Scalable and cost‑effective: FACS enrichment of spatially barcoded cells reduces sequencing costs, and the method uses commercially available 10x kits and custom microwell arrays that can be fabricated at scale

Why should we care?

PerturbSpace is a technical advance that adds spatial context to high‑throughput in vivo perturbation screens. It is best suited for questions about tissue‑level phenotypes (colony formation, regional composition, paracrine signaling) where 80 µm resolution suffices. It is not a replacement for imaging‑based spatial methods (MERFISH, Xenium) that achieve single‑cell or subcellular resolution. The most valuable contribution may be the demonstration that spatial hashing with universal antibodies can be integrated into standard single‑cell workflows, potentially lowering the barrier for many labs to adopt spatial functional genomics. However, the need for custom arrays and the resolution trade‑off mean that PerturbSpace will likely remain a specialized tool for the near future, not a routine method.

Spatially organized cancer-associated fibroblast subtypes partition cutaneous carcinomas into immune-active and contracted, immune-repressed niches

Aschenbrenner et al. bioRxiv (2026). 10.64898/2026.06.01.729186

The paper in one sentence

Using high-plex imaging mass cytometry, this study identifies four spatially organized cancer-associated fibroblast (CAF) subtypes in basal and squamous cell carcinomas, showing that myoCAFs (contractile, αSMA⁺) define immune-poor, aggressive tumor niches, whereas iCAFs (inflammatory) associate with immune-active, checkpoint-high microenvironments.

Summary

Cutaneous basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) differ markedly in invasiveness and metastatic risk, but the stromal mechanisms underlying these differences remain incompletely understood. The authors performed 33-plex imaging mass cytometry (IMC) on 28 regions of interest from 17 human tumors (4 nodular BCC, 5 infiltrative/sclerosing BCC, 3 well-differentiated SCC, 5 poorly differentiated SCC), generating a spatially resolved single-cell atlas of >739,000 cells. They identified four fibroblast populations: reticular fibroblasts (retFIBs; healthy dermal fibroblasts), immunomodulatory CAFs (iCAFs; MMP1⁺/IDO1⁺), matrix CAFs (mCAFs; COL11A1⁺), and myofibroblast-like CAFs (myoCAFs; αSMA⁺/TAGLN⁺). Infiltrative BCC and poorly differentiated SCC showed increased stromal area, extracellular matrix deposition, and a shift toward myoCAF-dominated stroma compared with their less aggressive counterparts. Spatial neighborhood analysis revealed that iCAFs localize to immune-rich, inflamed niches with elevated T cell activation and checkpoint markers, whereas myoCAFs occupy fibroblast-dense, immune-poor regions with globally reduced immune activation. mCAFs preferentially associate with stromal immune compartmentalization, limiting immune cell entry into tumor nests. At the invasive front, iCAF density correlated with antigen-experienced T cell states, while myoCAF density correlated with immune exclusion. In vitro, patient-derived CAFs from aggressive tumors showed enhanced collagen-gel contraction, and MCAM⁺ (myoCAF-like) cells were enriched in contractile cultures. Finally, stromal nuclear YAP/TAZ was elevated in aggressive tumor subtypes, and single-cell transcriptomic reanalysis revealed that mechanotransduction-associated programs are enriched in RGS5⁺/myoCAF-like cells, whereas classical YAP/TAZ transcriptional signatures are not uniformly increased across CAF subsets. The study proposes that CAF composition—particularly the balance between iCAFs and myoCAFs—stratifies immune topology and may influence therapeutic responsiveness.

Personal highlights

1. Four spatially distinct CAF subtypes in skin cancer: IMC resolves retFIBs (normal dermal fibroblasts), iCAFs (MMP1⁺/IDO1⁺, inflammatory), mCAFs (COL11A1⁺, matrix-producing), and myoCAFs (αSMA⁺/TAGLN⁺, contractile). Protein-level definitions differ from transcript-based CAF classifications, with ACTA2 RNA poorly correlating with αSMA protein in situ.

2. Aggressive tumor subtypes show myoCAF enrichment: Infiltrative/sclerosing BCC and poorly differentiated SCC have larger stromal areas, increased ECM deposition, and higher myoCAF densities compared with nodular BCC and well-differentiated SCC, suggesting a shift from matrix-producing to contractile stromal programs.

3. CAF subtypes define distinct immune niches: iCAFs correlate positively with multiple immune lineages and localize to lymphocyte-rich, inflamed neighborhoods with elevated activation and checkpoint markers (PD-1, LAG-3, Granzyme B). By contrast, myoCAFs occupy fibroblast-dense, immune-poor regions and are negatively correlated with immune cell densities, indicating that CAF identity, not fibroblast abundance alone, determines local immune contexture.

4. Spatial organization differs from compositional changes: Subtype differences in cell-cell neighborhood enrichment do not simply mirror cell abundance changes. For example, myoCAFs are more abundant in INF BCC, yet many myoCAF-associated heterotypic neighborhoods are not stronger in INF BCC, indicating spatial rewiring beyond compositional shifts.

5. Functional validation links myoCAFs to contractility and mechanotransduction: Patient-derived CAFs from aggressive tumors show enhanced collagen-gel contraction, and MCAM⁺ (myoCAF-like) cells correlate with contractile capacity. Stromal nuclear YAP/TAZ is elevated in aggressive subtypes, and single-cell transcriptomics shows that mechanotransduction-input programs are enriched in RGS5⁺/myoCAF-like cells, whereas ECM/CAF matrix programs are enriched in mCAFs.

Why should we care?

The main takeaway of this work is that the "stroma" is not a uniform support structure but a heterogeneous ecosystem where different fibroblast populations create either immune-permissive or immune-exclusionary niches. This framework could guide future biomarker development and combination therapies (e.g., targeting myoCAFs to relieve immune exclusion). But the work is hypothesis-generating, not clinically actionable. Confirmation in larger, prospective cohorts with outcome data and functional perturbation experiments (e.g., CAF subset depletion in mouse models) is required before CAF subtype composition can be used to stratify patients.

Multiplexed perturbation enables scalable pooled screens

Oberlin et al. Nature Methods (2026). 10.1038/s41592-026-03095-w

The paper in one sentence

Delivering multiple CRISPR guide RNAs per cell (multiplicity of infection, MOI, of 2.5–10) can maintain or improve pooled CRISPR interference screen performance while reducing required cell numbers by up to 10-fold, enabling genome-wide screens with as few as half a million sorted cells.

Summary

Pooled CRISPR screens typically require large cell numbers (50–100 million) to maintain adequate sgRNA representation (200–500× coverage), which is prohibitive for primary cells, in vivo models, or resource-limited settings. The authors investigate whether co‑delivering multiple sgRNAs per cell via high MOI lentiviral transduction can compress screens while preserving performance. Using K‑562 CRISPRi cells (dCas9-Zim3), they first show that infection efficiency plateaus at ~30 copies per cell and that mean fluorescence intensity (MFI) of a reporter correlates linearly with copy number, providing a simple proxy for MOI. They demonstrate simultaneous knockdown of up to five surface markers at MOI 5, with ~75% of cells carrying ≥5 sgRNAs repressing all five markers. Using a compact 2,000‑sgRNA library targeting epigenetic regulators, they benchmark essential gene and drug‑resistance screens across MOIs (0.3–30) and cell numbers. In the constant cell number condition (250× coverage), MOI 2.5–10 yields higher area under the curve (AUC) for essential gene identification than MOI 0.3. In the constant sgRNA condition (reducing cells proportionally to MOI), MOI 2.5–5 compensates for 2.5–5‑fold fewer cells, maintaining performance that drops sharply in low‑MOI controls. For drug‑tolerance (imatinib resistance), moderate MOI (2.5–5) improves hit detection, with true‑positive rates exceeding 0.75. Finally, they apply optimized conditions (MOI 5) to a genome‑wide CRISPRi screen for regulators of ICAM‑1 (CD54) using as few as 0.5 million sorted cells (25× coverage), identifying known and novel regulators (e.g., TRAF6, AMBRA1, PTPN1/2). Accuracy and true‑positive rates at MOI 5 with 25× coverage match or exceed those of standard low‑MOI screens with 250× coverage

Personal highlights

1. Simple MOI quantification via fluorescence: MFI of a reporter (eGFP, mOrange2, mBFP2) correlates linearly with sgRNA copy number (validated by digital PCR) up to the integration plateau (~30 copies). A low‑MOI sample (single insertion) serves as a reference to extrapolate MOI, eliminating the need for specialized equipment.

2. Moderate MOI (2.5–10) improves essential gene detection: In constant cell number screens (250× coverage), MOI 2.5–10 achieves higher ROC‑AUC for identifying cell‑essential genes than standard MOI 0.3, with true‑positive rates increasing by up to 11%. Higher MOI (20–30) causes performance decline, likely due to sgRNA collisions or toxicity.

3. Multiplexing compensates for reduced cell numbers: Screens at MOI 2.5–5 can reduce cell numbers by 2.5–5‑fold (e.g., from 250× to 50× coverage) without loss of performance, whereas low‑MOI controls show progressive AUC decline. This is most pronounced for low‑abundance sgRNAs, indicating that multiplexing buffers against bottleneck losses.

Why should we care?

The key takeaway is that multiplexing sgRNAs is a simple, cost‑effective strategy to compress CRISPR screens without sacrificing data quality. But it is not a magic bullet: careful titration, validation of infection efficiency, and awareness of potential combinatorial effects are essential. The method is best suited for genome‑wide screens in robust cell lines where the goal is to identify strong hits (e.g., essential genes or drug‑resistance drivers), not for subtle or highly context‑dependent phenotypes where single‑guide precision is critical. As the authors note, pilot testing is strongly advised. Nevertheless, the framework is a valuable addition to the CRISPR toolbox, particularly for labs with limited cell numbers or budgets.

Geometry-First Generative Spatial Single-Cell Reconstruction

Azim et al. Arxiv (2026). DOI: 10.1145/3770855.3818141

The paper in one sentence

GEARS reconstructs continuous 2D spatial coordinates for dissociated single cells by learning to generate intrinsic tissue geometry from expression alone, using spatial transcriptomics as pose-invariant geometric supervision without requiring cell-type labels, histological images, or cell-to-spot assignment.

Summary

Single-cell RNA sequencing (scRNA-seq) provides deep transcriptomic profiles but destroys spatial context, while spatial transcriptomics (ST) preserves tissue structure at lower resolution with fewer spots. Most existing integration methods either deconvolve spot mixtures or map single cells onto the measured ST lattice, tying reconstructions to a fixed grid and slide-specific coordinate system—a limitation that becomes severe when scRNA-seq and ST come from unpaired samples (different individuals or tissue sections). The authors propose GEARS, a geometry-first framework that treats spatial reconstruction as generating a continuous intrinsic geometry for dissociated cells, guided by ST but not constrained to its absolute coordinates. GEARS first trains a domain-invariant encoder (combining VICReg and adversarial domain alignment) to align ST spot profiles and scRNA-seq profiles in a shared embedding space. Then, from ST slides, it samples many overlapping local minisets and trains a permutation-equivariant set model (Set Transformer) with an EDM-preconditioned residual diffusion refiner to predict local geometries under pose-invariant supervision derived from Gram matrices (which encode intrinsic distances, invariant to rotation/reflection). At inference, GEARS encodes all scRNA-seq cells, samples overlapping patches, generates local geometries, converts them to pairwise distance measurements, stitches distances via reliability-weighted aggregation, and solves a global distance-geometry problem to obtain canonical 2D coordinates. Extensive benchmarking on a seqFISH mouse embryo atlas (where single-cell ground-truth coordinates exist) and a human squamous cell carcinoma dataset (cross-slide generalization) shows that GEARS improves global distance preservation, local neighborhood fidelity, and spatial distribution alignment over nine baselines, including Tangram, STEM, scSpace, and CytoSPACE. Ablation studies confirm that residual diffusion refinement substantially corrects global scale and distribution mismatches. GEARS also recovers unsupervised spatial domains in hSCC that align with cell-type annotations and validates that predicted pDC locations correspond to elevated expression of pDC markers (BST2, NRP1) on the reference ST slide.

Personal highlights

1. Geometry‑first, not spot‑lattice‑first: Unlike methods that force scRNA-seq cells onto measured ST spot coordinates, GEARS reconstructs an intrinsic continuous geometry for cells, using ST only as geometric supervision. This decouples reconstruction from slide‑specific coordinate frames and supports cross‑section generalization.

2. Pose‑invariant supervision via Gram matrices: Targets are derived from Gram matrices (VVᵀ) of centered local spot coordinates, which are invariant to rotation and reflection. This eliminates the need to align absolute orientations between different tissue sections.

3. Permutation‑equivariant generator with residual diffusion refinement: A Set Transformer backbone ensures predictions are invariant to the order of input cells. An EDM‑preconditioned diffusion model refines coarse generator proposals by denoising residuals, substantially improving global distance calibration (Stress‑1 drops by 34% on hSCC, SWD by 46%).

4. Patchwise distance‑first inference for large datasets: Instead of predicting coordinates for all cells at once, GEARS samples overlapping patches, generates local geometries, extracts pairwise distances, and stitches them via a reliability‑weighted median. This scales to large scRNA-seq cohorts while maintaining geometric fidelity across a wide range of patch sizes.

Other papers that peeked my interest and were added to the purgatory of my “to read” pile

• LAVA: Explainability for Unsupervised Latent Embeddings

• Global cell-state and gene-program representations reveal conserved and context-specific perturbation responses of cells

• Multiregional profiling reveals THBS1-SPP1 monocyte-macrophage axis drives immunosuppression and outcome in colorectal liver metastases

• Concurrent genetic and non-genetic resistance mechanisms to KRAS inhibition in colorectal cancer

• Conditional Monge Gap enables generalizable single-cell perturbation modelling

• Age distinguishes selection from causation in cancer genomes

• An organismal view of newborn cell dynamics in mammalian aging

• Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens

• Rational Design of Immune Gene Therapy Combinations via In Vivo CRISPR Activation Screen of Tumor Microenvironment Modulators

• A Tumor-Promoting Inflammatory SPP1⁺ Macrophage–IL6–CRP Axis Drives Immune Dysfunction in Bladder Cancer

• Single-Cell Spatial Atlas of High-Grade Serous Ovarian Cancer Uncovers MHC Class II as a Key Predictor of Spatial Tumor Ecosystems and Clinical Outcomes

• Single-cell spatial pharmacobiology identifies conserved stromal barriers to therapeutic antibody delivery in human solid tumors

• A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology.

Thanks for reading.

Cheers,

Seb.