Evidence map›Paper›PMID 40925369›Full record

ArticleCell systems2025

Flexible and robust cell-type annotation for highly multiplexed tissue images.

Huangqingbo Sun, Shiqiu Yu, Anna Martinez Casals, Anna Bäckström, Yuxin Lu, Cecilia Lindskog, Matthew Ruffalo, Emma Lundberg, Robert F Murphy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Huangqingbo SunRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Shiqiu YuRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Anna Martinez CasalsDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Anna BäckströmDepartment of Bioengineering, Stanford University, Stanford, CA, USA; Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology, and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.
Yuxin LuRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Cecilia LindskogDepartment of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Matthew RuffaloRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Emma LundbergDepartment of Bioengineering, Stanford University, Stanford, CA, USA; Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology, and Health, KTH-Royal Institute of Technology, Stockholm, Sweden; Department of Pathology, Stanford University, Stanford, CA, USA; Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA. Electronic address: emmalu@stanford.edu.
Robert F MurphyRay and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. Electronic address: murphy@cmu.edu.

Funding

Computational tools for uniform processing and integration of human reference atlas data [2 of 5]OT2OD033761 · OD · CARNEGIE-MELLON UNIVERSITY · PI PATEN, BENEDICT, RUFFALO, MATTHEW · 2022 to 2025
$4.2M
PROMINENT - StanfordOT2CA278713 · NCI · STANFORD UNIVERSITY · PI Emma Lundberg · 2022 to 2026
$3.7M
NCI NIH HHS OT2 CA278713NIH HHS OT2 OD033761
6 · The paper itself

Abstract

Identifying cell types in highly multiplexed images is essential for understanding tissue spatial organization. Current cell-type annotation methods often rely on extensive reference images and manual adjustments. In this work, we present a tool, the Robust Image-Based Cell Annotator (RIBCA), that enables accurate, automated, unbiased, and fine-grained cell-type annotation for images with a wide range of antibody panels without requiring additional model training or human intervention. Our tool has successfully annotated over 3 million cells, revealing the spatial organization of various cell types across more than 40 different human tissues. It is open source and features a modular design, allowing for easy extension to additional cell types.

Indexed as

Image Processing, Computer-AssistedHumansSoftwarebioimage analysiscell-type annotationhighly multiplexed imagingmachine learningmarker imputationspatial proteomicsvision transformer

Identifiers

PMID40925369
PMCPMC12728825

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.