Evidence map›Paper›PMID 32161403›Full record

ArticleNature medicine2020

Multiplexed single-cell morphometry for hematopathology diagnostics.

Albert G Tsai, David R Glass, Marisa Juntilla, Felix J Hartmann, Jean S Oak, Sebastian Fernandez-Pol, Robert S Ohgami, Sean C Bendall

Open access · greenAbstract read
In one paragraph

Article in Nature medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
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

29 citing papers in PubMed, 46 citations in OpenAlex.

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  15. Single-Cell Analysis in Immuno-Oncology.International journal of molecular sciences · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Albert G Tsai *Department of Pathology, Stanford University, Stanford, CA, USA.
David R Glass *Department of Pathology, Stanford University, Stanford, CA, USA.
Marisa JuntillaDepartment of Pathology, Stanford University, Stanford, CA, USA.
Felix J HartmannDepartment of Pathology, Stanford University, Stanford, CA, USA.
Jean S OakDepartment of Pathology, Stanford University, Stanford, CA, USA.
Sebastian Fernandez-PolDepartment of Pathology, Stanford University, Stanford, CA, USA.
Robert S OhgamiDepartment of Pathology, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-1881-3440
Sean C BendallDepartment of Pathology, Stanford University, Stanford, CA, USA. bendall@stanford.edu.ORCID http://orcid.org/0000-0003-1341-2453
Stanford University · USUniversity of California, San Francisco · US

Funding

Stanford Cancer Immune Monitoring and Analysis Center (CIMAC)U24CA224309 · NCI · STANFORD UNIVERSITY · PI Sean Curtis Bendall, Holden T. Maecker · 2017 to 2026
$19.9M
Tracking allergen specific T cells in multi-food allergyU19AI104209 · NIAID · STANFORD UNIVERSITY · PI GALLI, STEPHEN JOSEPH · 2013 to 2025
$17.3M
Stanford/UNC Biomimetic U19 Research CenterU19AI116484 · NIAID · STANFORD UNIVERSITY · PI AMIEVA, MANUEL R, KUO, CALVIN J · 2015 to 2025
$13.5M
MIRIAD - Multiplexed Imaging of Resilience In Alzheimers DiseaseR01AG057915 · NIA · STANFORD UNIVERSITY · PI ANGELO, ROBERT MICHAEL, BENDALL, SEAN CURTIS · 2017 to 2021
$4.5M
The Phenotypic Landscape of Cognitive Decline as Revealed by Next-Generation Multiplexed Ion Beam ImagingR01AG056287 · NIA · STANFORD UNIVERSITY · PI ANGELO, ROBERT MICHAEL, BENDALL, SEAN CURTIS · 2017 to 2021
$2.6M
Origins of human blood lineages in regenerative medicineDP2EB024246 · NIBIB · STANFORD UNIVERSITY · PI BENDALL, SEAN CURTIS · 2016 to 2016
$2.4M
A robust platform for multiplexed, subcellular proteomic imaging in human tissueUH3CA246633 · NCI · STANFORD UNIVERSITY · PI ANGELO, ROBERT MICHAEL, BENDALL, SEAN CURTIS · 2019 to 2021
$1.8M
A single-cell platform to discover and study regulators of human developmentR00GM104148 · NIGMS · STANFORD UNIVERSITY · PI BENDALL, SEAN CURTIS · 2014 to 2016
$747k
Damon Runyon Cancer Research Foundation DRG-2017-09NCI NIH HHS U24 CA224309NCI NIH HHS UH3 CA246633NIAID NIH HHS U19 AI104209NIAID NIH HHS U19 AI116484NIA NIH HHS R01 AG056287NIA NIH HHS R01 AG057915NIBIB NIH HHS DP2 EB024246NIGMS NIH HHS R00 GM104148
6 · The paper itself

Abstract

The diagnosis of lymphomas and leukemias requires hematopathologists to integrate microscopically visible cellular morphology with antibody-identified cell surface molecule expression. To merge these into one high-throughput, highly multiplexed, single-cell assay, we quantify cell morphological features by their underlying, antibody-measurable molecular components, which empowers mass cytometers to 'see' like pathologists. When applied to 71 diverse clinical samples, single-cell morphometric profiling reveals robust and distinct patterns of 'morphometric' markers for each major cell type. Individually, lamin B1 highlights acute leukemias, lamin A/C helps distinguish normal from neoplastic mature T cells, and VAMP-7 recapitulates light-cytometric side scatter. Combined with machine learning, morphometric markers form intuitive visualizations of normal and neoplastic cellular distribution and differentiation. When recalibrated for myelomonocytic blast enumeration, this approach is superior to flow cytometry and comparable to expert microscopy, bypassing years of specialized training. The contextualization of traditional surface markers on independent morphometric frameworks permits more sensitive and automated diagnosis of complex hematopoietic diseases.

Indexed as

Hematopoietic Stem CellsHumansLaminsLeukemiaLeukocyte Common AntigensLymphomaMyeloid CellsR-SNARE ProteinsSingle-Cell AnalysisLaminsLeukocyte Common AntigensR-SNARE ProteinsVAMP7 protein, human

Identifiers

PMID32161403
PMCPMC7301910
OpenAlexW3011143950

What OpenQuestion holds

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Registered trials

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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.