OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data DOI Creative Commons

Rishika Chowdary,

Robert K. Suter, Matthew D’Antuono

et al.

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

Published: Nov. 26, 2024

ABSTRACT Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses small molecules use transcriptome-based efforts. However, pre-clinical research often requires vivo experiments non-human species, which makes capitalizing on such valuable resources difficult. To facilitate application pharmacotranscriptomic models orthologous conversion transcriptomes, we introduce OrthologAL. OrthologAL leverages BioMart database access different sets from Ensembl, facilitating interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional any will output a ortholog-converted dataset download use. demonstrate utility this application, characterized single-cell, single-nuclei, transcriptomic derived common models, including patient-derived orthotopic xenografts medulloblastoma, mouse rat spinal cord injury. We show convert types efficiently corresponding orthologs while preserving dimensional architecture original data. be broadly useful applying pre-clinical, functional molecule predictions using existing human-annotated databases.

Language: Английский

Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling DOI Creative Commons
Michelli F. Oliveira, Juan P. Romero,

Meii Chung

et al.

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

Published: June 5, 2024

Abstract Colorectal cancer (CRC) is the second-deadliest in world, yet a deeper understanding of spatial patterns gene expression tumor microenvironment (TME) remains elusive. Here, we introduce Visium HD platform (10x Genomics) and use it to investigate human CRC normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on probe-based transcriptomics workflow that was developed enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised clustering data aligns with hallmarks colon tissue morphology notably improved over earlier assays. Using serial sections same FFPE blocks generate atlas our samples, then integrate comprehensively characterize immune types present TME, specifically at periphery. observed enrichment two pro-tumor macrophage subpopulations differential profiles were localized within distinct regions. Further characterization T cells one samples revealed clonal expansion able localize using situ In analysis also allowed us perform in-depth clonally expanded population identified third subpopulation consistent an anti-tumor response. Our study provides comprehensive map cellular composition TME identifies phenotypically spatially populations it. show cell-scale resolution afforded by nature allows investigations into function interaction periphery tissues, which has not been previously possible.

Language: Английский

Citations

46
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
Systematic inference of super-resolution cell spatial profiles from histology images DOI Creative Commons
Peng Zhang, Chaofei Gao, Zhuoyu Zhang

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 21, 2025

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution consisting of types, states their network at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance terms type/states prediction solely across multiple tissues. can significantly enhance deconvolution accuracy transcriptomics data enable accurate annotation subtle tissue architectures. Moreover, applied de novo discovery clinically relevant organization indicators, including prognosis drug response biomarkers, diverse types. also image-based screening populations drives phenotype interest, discover population corresponding indicators associated with gastric malignant transformation risk. Overall, emerges as powerful versatile tool studies image-only cohorts. The significance inferring patients remains be explored. Here, authors develop direct

Language: Английский

Citations

1
A practical guide to spatial transcriptomics DOI
Lukás Valihrach, Daniel Žucha, Pavel Abaffy

et al.

Molecular Aspects of Medicine, Journal Year: 2024, Volume and Issue: 97, P. 101276 - 101276

Published: May 21, 2024

Language: Английский

Citations

4
Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity DOI Creative Commons
Yixing Dong, Chiara Saglietti, Quentin Bayard

et al.

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

Published: Nov. 3, 2024

Abstract Recent advancements in probe-based, full-transcriptome, high-resolution technologies for Formalin-Fixed Paraffin-Embedded (FFPE) tissues, such as Visium CytAssist, Chromium Flex (10X Genomics), and GeoMx DSP (Nanostring), have opened new opportunities studying decades-old archival samples biobanks, facilitating the generation of data from extensive cohorts. However, experimental protocols can be labor-intensive costly; therefore, it is thus essential researchers to carefully evaluate strengths limitations each technology relation their specific research objectives. Here, we report results a comparative analysis three methods mentioned above on FFPE tumor four non-small cell lung cancer, breast cancer six diffuse large B-cell lymphoma. We highlight some relative advantages disadvantages method context operational challenges, bioinformatic biological discovery. Our show that: 1) all yielded good-quality, highly reproducible transcriptomic serial sections same block; 2) contained mixtures types, even when pre-selecting areas with type-specific markers; 3) high-throughput spot-level (Visium) or cell-level (Chromium) enabled identification heterogeneity within between patients, which could used identify targeted therapies. support use discovery-driven projects, while platform suited addressing specialized questions regions. All generated this study, including GeoMx, Visium, Chromium, H&E, expert annotations are publicly available.

Language: Английский

Citations

1
Pipeline for Assessing Tumor Immune Status Using Superplex Immunostaining and Spatial Immune Interaction Analysis DOI Creative Commons
Chaoxin Xiao,

Ruihan Zhou,

Qin Chen

et al.

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

Published: Aug. 26, 2024

ABSTRACT The characteristics of the tumor microenvironment (TME) are closely linked to progression and treatment response. TME comprises various cell types, their spatial distribution, cell-cell interactions, organization into cellular niches or neighborhoods. To capture this complexity, several profiling technologies have been developed. However, challenges such as low throughput, high costs, complicated data analysis limited widespread use in immune research. In study, we introduce Cyclic-multiplex TSA (CmTSA) staining platform, a high-throughput superplex technology based on tyramide signal amplification (TSA) immunostaining combined with an efficient fluorophore recycling method. CmTSA platform allows for labeling 30-60 antigens across multiple parallel formalin-fixed paraffin-embedded (FFPE) slides. Furthermore, automated workflow requires only standard histological equipment conventional immunohistochemistry (IHC) primary antibodies (Abs), significantly reducing costs. While images produced contain extensive multidimensional information, extracting features from raw pixel can be challenging. address this, present computer vision-based pipeline, which begins deep learning-based algorithms segment individual cells identify types defined annotation rules. It then evaluates distribution tendencies each type, interaction intensity between paired cells, multicellular functional niches. This comprehensive approach enables researchers visualize quantify states, levels activities within effectively, advancing immunology research precision medicine.

Language: Английский

Citations

0
OrthologAL: A Shiny application for quality-aware humanization of non-human pre-clinical high-dimensional gene expression data DOI Creative Commons

Rishika Chowdary,

Robert K. Suter, Matthew D’Antuono

et al.

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

Published: Nov. 26, 2024

ABSTRACT Single-cell and spatial transcriptomics provide unprecedented insight into the inner workings of disease. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing treatment discovery in many diseases. Multiple databases attempt to connect human cellular transcriptional responses small molecules use transcriptome-based efforts. However, pre-clinical research often requires vivo experiments non-human species, which makes capitalizing on such valuable resources difficult. To facilitate application pharmacotranscriptomic models orthologous conversion transcriptomes, we introduce OrthologAL. OrthologAL leverages BioMart database access different sets from Ensembl, facilitating interaction between these servers without needing user-generated code. Researchers can input their single-cell or other high-dimensional any will output a ortholog-converted dataset download use. demonstrate utility this application, characterized single-cell, single-nuclei, transcriptomic derived common models, including patient-derived orthotopic xenografts medulloblastoma, mouse rat spinal cord injury. We show convert types efficiently corresponding orthologs while preserving dimensional architecture original data. be broadly useful applying pre-clinical, functional molecule predictions using existing human-annotated databases.

Language: Английский

Citations

0