Evidence map›Paper›PMID 33762607›Full record

ArticleScientific reports2021

Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging.

Sara Imboden, Xuanqing Liu, Brandon S Lee, Marie C Payne, Cho-Jui Hsieh, Neil Y C Lin

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
6.5field-weighted citation impact, top 4% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

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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 1 institution in 1 country.

Sara Imboden *Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, 90095, USA. imbodens@ethz.ch.
Xuanqing Liu *Department of Computer Science, University of California, Los Angeles, 90095, USA.
Brandon S LeeDepartment of Bioengineering, University of California, Los Angeles, 90095, USA.
Marie C PayneDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles, 90095, USA.
Cho-Jui HsiehDepartment of Computer Science, University of California, Los Angeles, 90095, USA.
Neil Y C LinDepartment of Mechanical and Aerospace Engineering, University of California, Los Angeles, 90095, USA.
University of California, Los Angeles · US

Funding

UCLA SPORE IN PROSTATE CANCERP50CA092131 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI REITER, ROBERT E · 2002 to 2023
$42.6M
NCI NIH HHS P50 CA092131
6 · The paper itself

Abstract

Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean [Formula: see text] = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.

Indexed as

Artificial IntelligenceBiomarkersMolecular ImagingStaining and LabelingComputational BiologyFlow CytometryFluorescent Antibody TechniqueGene ExpressionGene Expression ProfilingImage Processing, Computer-AssistedMesenchymal Stem CellsBiomarkers

Identifiers

PMID33762607
PMCPMC7991643
OpenAlexW3138737508

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.