Systematic Benchmarking of High-Throughput Subcellular Spatial Transcriptomics Platforms DOI Creative Commons
Pengfei Ren, Rui Zhang, Yunfeng Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Abstract Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. To address this, we collected clinical samples from three cancer types – colon adenocarcinoma, hepatocellular carcinoma, ovarian generated serial tissue sections evaluation. Using these uniformly processed samples, data across five high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, FF, CosMx 6K, Xenium 5K. establish ground truth datasets, profiled proteins adjacent corresponding to all using CODEX performed single-cell RNA sequencing on the same samples. Leveraging manual cell segmentation detailed annotations, systematically assessed each platform’s performance key metrics, including capture sensitivity, specificity, diffusion control, segmentation, annotation, clustering, transcript-protein alignment CODEX. The generated, processed, annotated multi-omics dataset is valuable advancing computational method development biological discoveries. accessible via SPATCH, a user-friendly web server visualization download ( http://spatch.pku-genomics.org/ ).

Language: Английский

Open-ST: High-resolution spatial transcriptomics in 3D DOI

Marie Schott,

Daniel León-Periñán, Elena Splendiani

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(15), P. 3953 - 3972.e26

Published: June 24, 2024

Language: Английский

Citations

37
Comparative analysis of multiplexed in situ gene expression profiling technologies DOI Open Access
Austin Hartman, Rahul Satija

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 12, 2024

The burgeoning interest in situ multiplexed gene expression profiling technologies has opened new avenues for understanding cellular behavior and interactions. In this study, we present a comparative benchmark analysis of six methods, including both commercially available academically developed using publicly accessible mouse brain datasets. We find that standard sensitivity metrics, such as the number unique molecules detected per cell, are not directly comparable across datasets due to substantial differences incidence off-target molecular artifacts impacting specificity. To address these challenges, explored various potential sources artifacts, novel metrics control them, utilized evaluate compare different technologies. Finally, demonstrate how false positives can seriously confound spatially-aware differential analysis, requiring caution interpretation downstream results. Our provides guidance selection, processing, spatial

Language: Английский

Citations

18
Multiscale topology classifies cells in subcellular spatial transcriptomics DOI Creative Commons
Katherine Benjamin,

Aneesha Bhandari,

Jessica D. Kepple

et al.

Nature, Journal Year: 2024, Volume and Issue: 630(8018), P. 943 - 949

Published: June 19, 2024

Abstract Spatial transcriptomics measures in situ gene expression at millions of locations within a tissue 1 , hitherto with some trade-off between transcriptome depth, spatial resolution and sample size 2 . Although integration image-based segmentation has enabled impactful work this context, it is limited by imaging quality heterogeneity. By contrast, recent array-based technologies offer the ability to measure entire subcellular across large samples 3–6 Presently, there exist no approaches for cell type identification that directly leverage information annotate individual cells. Here we propose multiscale approach automatically classify types level, using both transcriptomic context. We showcase on targeted whole-transcriptome platforms, improving classification morphology human kidney pinpointing sparsely distributed renal mouse immune cells without reliance image data. integrating these predictions into topological pipeline based multiparameter persistent homology 7–9 identify relationships characteristic model lupus nephritis, which validate experimentally immunofluorescence. The proposed framework readily generalizes new providing comprehensive bridging different levels biological organization from genes through tissues.

Language: Английский

Citations

17
Comparative analysis of multiplexed in situ gene expression profiling technologies DOI Open Access
Austin Hartman, Rahul Satija

Published: June 7, 2024

The burgeoning interest in situ multiplexed gene expression profiling technologies has opened new avenues for understanding cellular behavior and interactions. In this study, we present a comparative benchmark analysis of six methods, including both commercially available academically developed using publicly accessible mouse brain datasets. We find that standard sensitivity metrics, such as the number unique molecules detected per cell, are not directly comparable across datasets due to substantial differences incidence off-target molecular artifacts impacting specificity. To address these challenges, explored various potential sources artifacts, novel metrics control them, utilized evaluate compare different technologies. Finally, demonstrate how false positives can seriously confound spatially-aware differential analysis, requiring caution interpretation downstream results. Our provides guidance selection, processing, spatial

Language: Английский

Citations

12
Comparison of spatial transcriptomics technologies across six cancer types DOI Creative Commons

Sergi Cervilla,

Daniela Grases,

Elena Pérez

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 21, 2024

Abstract Spatial biology experiments integrate the molecular and histological landscape of tissues to provide a previously inaccessible view tissue biology, unlocking architecture complex multicellular tissues. Within spatial transcriptomics platforms are among most advanced, allowing researchers characterize expression thousands genes across space. These new technologies transforming our understanding how cells organized in space communicate with each other determine emergent phenotypes unprecedented granularity. This is particularly important cancer research, as it becoming evident that tumor evolution shaped not only by genetic properties but also they interact microenvironment their organization. While many can generate profiles, still unclear which context platform better suits needs its users. Here we compare results obtained using 4 different (VISIUM, VISIUM CytAssist, Xenium CosMx) one proteomics (VISIUM CytAssist) serial sections 6 FFPE samples from primary human tumors covering some common forms disease (lung, breast, colorectal, bladder, lymphoma ovary). We observed CytAssist chemistry yielded superior data quality. consistently produced more reliable for situ platforms, gene clustering fewer false positives than CosMx. Interestingly, these platform-based variations didn’t significantly affect cell type identification. Finally, comparing protein profiles all four on sample, identified several mismatched RNA patterns, highlighting importance multi-omics profiling reveal true tumors.

Language: Английский

Citations

11
Interneuron diversity in the human dorsal striatum DOI Creative Commons
Leonardo D. Garma, Lisbeth Harder, Juan M. Barba-Reyes

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 22, 2024

Abstract Deciphering the striatal interneuron diversity is key to understanding basal ganglia circuit and untangling complex neurological psychiatric diseases affecting this brain structure. We performed snRNA-seq spatial transcriptomics of postmortem human caudate nucleus putamen samples elucidate abundance populations their inherent transcriptional structure in dorsal striatum. propose a comprehensive taxonomy interneurons with eight main classes fourteen subclasses, providing full transcriptomic identity expression profile as well additional quantitative FISH validation for specific populations. have also delineated correspondence our previous standardized classifications shown class differences between putamen. Notably, based on functional genes such ion channels synaptic receptors, we found matching known mouse most abundant populations, recently described PTHLH TAC3 interneurons. Finally, were able integrate other published datasets ours, supporting generalizability harmonized taxonomy.

Language: Английский

Citations

11
Nicheformer: A Foundation Model for Single-Cell and Spatial Omics DOI
Anna C. Schaar, Alejandro Tejada-Lapuerta, Giovanni Palla

et al.

Published: Jan. 1, 2024

Tissue makeup relies fundamentally on the cellular microenvironment. Spatial single-cell genomics allows probing underlying interactions in an unbiased, scalable fashion. To learn a unified cell representation that accounts for local dependencies microenvironment, we propose Nicheformer, transformer-based foundation model combines human and mouse dissociated targeted spatial transcriptomics data. Pretrained over 57 million 53 spatially resolved cells across 73 tissues, is fine-tuned tasks omics data to decode information. Nicheformer excels zero-shot-like fine-tuning scenarios novel set of downstream tasks, particular composition prediction label prediction. enables context cells, allowing transfer rich information scRNA-seq datasets. Overall, sets stage next generation machine-learning models analysis.

Language: Английский

Citations

9
Points to Consider From the ESTP Pathology 2.0 Working Group: Overview on Spatial Omics Technologies Supporting Drug Discovery and Development DOI
Kerstin Hahn, Bettina Amberg, Josep M. Monné Rodríguez

et al.

Toxicologic Pathology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Recent advances in bioanalytical and imaging technologies have revolutionized our ability to assess complex biological pathological changes within tissue samples. Spatial omics, a rapidly evolving technology, enables the simultaneous detection of multiple biomolecules sections, allowing for high-dimensional molecular profiling microanatomical contexts. This offers powerful opportunity precise, multidimensional exploration disease pathophysiology. The Pathology 2.0 working group European Society Toxicologic (ESTP) includes subgroup dedicated spatial omics technologies. Their primary goal is raise awareness about these emerging their potential applications discovery toxicologic pathology. review provides an overview commonly used, commercially available platforms transcriptomic, proteomic, multiomic analysis, discussing technical aspects illustrative examples applications. To harness power translational drug human safety risk assessment, we emphasize important role pathologists at every stage workflow—from hypothesis generation sample preparation, data interpretation. offer novel opportunities target discovery, lead selection, preclinical clinical development compound development.

Language: Английский

Citations

1
Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows DOI Creative Commons
Sergio Marco Salas,

Louis B. Kuemmerle,

Christoffer Mattsson-Langseth

et al.

Nature Methods, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds genes in situ at subcellular resolution. Given the multitude commercially available technologies, recommendations choice and analysis guidelines are increasingly important. Herein, we explore 25 datasets generated from multiple tissues species, comparing scalability, resolution, data quality, capacities limitations with eight other spatially resolved technologies commercial platforms. addition, benchmark performance open-source computational tools, when applied to datasets, tasks including preprocessing, cell segmentation, selection variable features domain identification. This study serves as an independent Xenium, provides best practices for such datasets.

Language: Английский

Citations

1
Comparison of spatial transcriptomics technologies using tumor cryosections DOI Creative Commons
Anne Rademacher, Alik Huseynov, Michele Bortolomeazzi

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 5, 2024

Abstract Background Spatial transcriptomics ( ST ) technologies are revolutionizing our understanding of intra-tumor heterogeneity and the tumor microenvironment by revealing single-cell molecular profiles within their spatial tissue context. The rapid evolution methods, each with unique features, presents a challenge in selecting most appropriate technology for specific research objectives. Here, we compare four imaging-based methods – RNAscope HiPlex, Molecular Cartography, MERFISH/Merscope, Xenium together sequencing-based (Visium). These were used to study cryosections medulloblastoma extensive nodularity (MBEN), chosen its distinct microanatomical features. Results Our analysis reveals that automated well suited delineating intricate MBEN microanatomy, capturing cell-type-specific transcriptome profiles. We devise approaches sensitivity specificity different attributes guide method selection based on aim. Furthermore, demonstrate how reimaging slides after can markedly improve cell segmentation accuracy integrate additional transcript protein readouts expand analytical possibilities depth insights. Conclusions This highlights key distinctions between various provides set parameters evaluating performance. findings aid informed choice delineate enhancing resolution breadth transcriptomic analyses, thereby contributing advancing applications solid research.

Language: Английский

Citations

8