Evidence map›Paper›PMID 38466569›Full record

ArticleCancer research communications2024

Spectral Flow Cytometry Methods and Pipelines for Comprehensive Immunoprofiling of Human Peripheral Blood and Bone Marrow.

Milos Spasic, Esther R Ogayo, Adrienne M Parsons, Elizabeth A Mittendorf, Peter van Galen, Sandra S McAllister

Open access · goldAbstract read
In one paragraph

Article in Cancer research communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

22 citing papers in PubMed, 19 citations in OpenAlex.

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  15. Spectral Flow Cytometry: The Current State and Future of the Technology.International journal of molecular sciences · 2025
    Review
  16. Lipid Metabolism and Immune Function: Chemical Tools for Insights into T-Cell Biology.Chembiochem : a European journal of chemical biology · 2025
    Review
  17. Review
  18. Article
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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

6 authors at 2 institutions in 1 country.

Milos Spasic *Division of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0000-0001-5451-7003
Esther R Ogayo *Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0009-0007-9661-7951
Adrienne M ParsonsDivision of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0000-0002-9878-9403
Elizabeth A MittendorfDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0000-0002-9762-8536
Peter van GalenDivision of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0000-0002-0735-1570
Sandra S McAllisterDivision of Hematology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.ORCID 0000-0002-5111-5903
Brigham and Women's Hospital · USBroad Institute · US

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
Understanding the impact of chemotherapy on breast cancer metastasis and immune function in the liverR01CA279959 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI Sandra S McAllister · 2023 to 2026
$2.0M
Clonal analysis of cancer by mitochondrial DNA barcodingR33CA278393 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI SANKARAN, VIJAY GANESH, VAN GALEN, PETER · 2023 to 2025
$1.3M
NCI NIH HHS R01 CA279959NCI NIH HHS R33 CA278393NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

Profiling hematopoietic and immune cells provides important information about disease risk, disease status, and therapeutic responses. Spectral flow cytometry enables high-dimensional single-cell evaluation of large cohorts in a high-throughput manner. Here, we designed, optimized, and implemented new methods for deep immunophenotyping of human peripheral blood and bone marrow by spectral flow cytometry. Two blood antibody panels capture 48 cell-surface markers to assess more than 58 cell phenotypes, including subsets of T cells, B cells, monocytes, natural killer (NK) cells, and dendritic cells, and their respective markers of exhaustion, activation, and differentiation in less than 2 mL of blood. A bone marrow antibody panel captures 32 markers for 35 cell phenotypes, including stem/progenitor populations, T-cell subsets, dendritic cells, NK cells, and myeloid cells in a single tube. We adapted and developed innovative flow cytometric analysis algorithms, originally developed for single-cell genomics, to improve data integration and visualization. We also highlight technical considerations for users to ensure data fidelity. Our protocol and analysis pipeline accurately identifies rare cell types, discerns differences in cell abundance and phenotype across donors, and shows concordant immune landscape trends in patients with known hematologic malignancy. SIGNIFICANCE: This study introduces optimized methods and analysis algorithms that enhance capabilities in comprehensive immunophenotyping of human blood and bone marrow using spectral flow cytometry. This approach facilitates detection of rare cell types, enables measurement of cell variations across donors, and provides proof-of-concept in identifying known hematologic malignancies. By unlocking complexities of hematopoietic and immune landscapes at the single-cell level, this advancement holds potential for understanding disease states and therapeutic responses.

Indexed as

Bone MarrowMonocytesFlow CytometryHumansImmunophenotypingMyeloid Cells

Identifiers

PMID38466569
PMCPMC10962315
OpenAlexW4392642281

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.