Evidence map›Paper›PMID 38014067›Full record

ArticlebioRxiv : the preprint server for biology2023

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data.

Guihong Wan, Zoltan Maliga, Boshen Yan, Tuulia Vallius, Yingxiao Shi, Sara Khattab, Crystal Chang, Ajit J Nirmal, Kun-Hsing Yu, David Liu and 4 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Guihong WanDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-1100-4018
Zoltan MaligaLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA.
Boshen YanDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Tuulia ValliusLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA.
Yingxiao ShiDepartment of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA.
Sara KhattabDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Crystal ChangDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Ajit J NirmalLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA.
Kun-Hsing YuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-9892-8218
David LiuDepartment of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA.
Christine G LianDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Mia S DeSimoneDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Peter K SorgerLaboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA.ORCID 0000-0002-3364-1838
Yevgeniy R SemenovDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Funding

Robust, Generalizable, and Fair Machine Learning Models for BiomedicineR35GM142879 · NIGMS · HARVARD MEDICAL SCHOOL · PI YU, KUN-HSING · 2021 to 2025
$2.4M
Predictive modeling of cutaneous immune checkpoint inhibitor toxicitiesK23AR080791 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Yevgeniy R Semenov · 2023 to 2026
$704k
Explainable Artificial Intelligence for Melanoma Recurrence Prediction via Integrative Modeling of Multiplexed Imaging with Transcriptomics and HistopathologyK99CA286966 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI WAN, GUIHONG · 2024 to 2025
$352k
NCI NIH HHS K99 CA286966NIAMS NIH HHS K23 AR080791NIGMS NIH HHS R35 GM142879
6 · The paper itself

Abstract

Background: Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results: This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions: SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.

Indexed as

BioinformaticsImmune infiltrationMultiplexed imagingRegion-based profilingSingle-cell dataSpatial analysisTumor microenvironment

Identifiers

PMID38014067
PMCPMC10680639

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.