An approach for extensibly profiling the molecular states of cellular subpopulations
Open accessAbstract
Microscopy often reveals the existence of phenotypically distinct cellular subpopulations. However, additional characterization of observed subpopulations can be limited by the number of biomolecular markers that can be simultaneously monitored. Here we present a computational approach for extensibly profiling cellular subpopulations by freeing one or more imaging channels to monitor additional probes. In our approach, we trained classifiers to re-identify subpopulations accurately based on an enhanced collection of phenotypic features extracted from only a subset of the original markers. Then we constructed subpopulation profiles step-wise from replicate experiments, in which cells were labeled with different but overlapping marker sets. We applied our approach to identify molecular differences among subpopulations and to identify functional groupings of markers, in populations of differentiating mouse preadipocytes, polarizing human neutrophil-like cells and dividing human cancer cells.
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The Z-score oversimplifies interpretation and fails to capture changes in the modality of population-level feature distributions or subpopulations with different responses.
“While the Z-score is commonly used to quantify phenotypic differences between treatment and control conditions23,28, it oversimplifies interpretation and will fail to capture changes in the modality of population-level feature distributions or subpopulations with different responses11.”
From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [11]
The Results (Validation of Virtual Phenotypic Profiling; page unavailable) show that distinct subpopulation profiles can differ significantly from a population-averaged profile, supporting the concern that a population-level summary can miss heterogeneity. The paper does not evaluate Z-scores, changes in distribution modality, or subpopulations with different treatment responses, so those specific claims are not established by this source. (LLM, conf=0.84)
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- Authors
- Loo, L.-H., Lin, H.-J., Steininger, R. J., Wang, Y., Wu, L. F., & Altschuler, S. J.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Wu, Lani F
- Altschuler, Steven J
- Venue
- Nature Methods
- Published
- 2009-09-20
- Volume
- 6
- Issue
- 10
- Pages
- 759-765
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC2818727
- OpenAlex
- W2056344676
- Cited by
- 69
- References
- 30
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 1 · 2022: 3 · 2021: 4 · 2020: 4 · 2019: 4 · 2018: 2 · 2017: 2 · 2016: 3 · 2015: 6 · 2014: 7 · 2013: 6 · 2012: 7
- FWCI
- 3.95
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- University of Texas Southwestern Medical Center
- National Institute of General Medical Sciences (R01GM081549, R01GM085442)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Keywords
- cell subpopulations, fluorescence imaging, cellular heterogeneity, phenotypic traits, preadipocyte differentiation, neutrophil polarization, cancer cell division
- MeSH terms
- Algorithms, Artificial Intelligence, Cells, Cultured, Pattern Recognition, Automated, Biomarkers, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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