Evidence map›Paper›PMID 36493779›Full record

ArticleStem cell reports2023

Integrated transcriptome-proteome analyses of human stem cells reveal source-dependent differences in their regenerative signature.

Abantika Ganguly, Ganesh Swaminathan, Fernando Garcia-Marques, Shobha Regmi, Reza Yarani, Rosita Primavera, Shashank Chetty, Abel Bermudez, Sharon J Pitteri, Avnesh S Thakor

Open access · goldAbstract read
In one paragraph

Article in Stem cell reports, 2023. 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
5.2field-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

29 citing papers in PubMed, 31 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

10 authors at 1 institution in 1 country.

Abantika GangulyInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA.
Ganesh SwaminathanInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA.
Fernando Garcia-MarquesCanary Center at Stanford for Cancer Early Detection, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304, USA.
Shobha RegmiInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA.
Reza YaraniInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA.
Rosita PrimaveraInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA.
Shashank ChettyInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA.
Abel BermudezCanary Center at Stanford for Cancer Early Detection, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304, USA.
Sharon J PitteriCanary Center at Stanford for Cancer Early Detection, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304, USA.
Avnesh S ThakorInterventional Radiology Innovation at Stanford (IRIS), Department of Radiology, School of Medicine, Stanford University, 3155 Porter Drive, Palo Alto, CA 94304, USA. Electronic address: asthakor@stanford.edu.
Stanford University · US

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Treating kidney injury using soundwaves combined with mesenchymal stem cells and their extracellular vesiclesR01DK129598 · NIDDK · STANFORD UNIVERSITY · PI Avnesh Sinh Thakor · 2021 to 2026
$2.8M
A novel approach for treating diabetes using pulsed focused ultrasound and intra-arterial delivery of mesenchymal stem cell based therapies directly into the pancreasR01DK119293 · NIDDK · STANFORD UNIVERSITY · PI THAKOR, AVNESH SINH · 2019 to 2023
$2.3M
NIDDK NIH HHS R01 DK119293NIDDK NIH HHS R01 DK129598
6 · The paper itself

Abstract

Mesenchymal stem cells (MSCs) are gaining increasing prominence as an effective regenerative cellular therapy. However, ensuring consistent and reliable effects across clinical populations has proved to be challenging. In part, this can be attributed to heterogeneity in the intrinsic molecular and regenerative signature of MSCs, which is dependent on their source of origin. The present work uses integrated omics-based profiling, at different functional levels, to compare the anti-inflammatory, immunomodulatory, and angiogenic properties between MSCs from neonatal (umbilical cord MSC [UC-MSC]) and adult (adipose tissue MSC [AD-MSC], and bone marrow MSC [BM-MSC]) sources. Using multi-parametric analyses, we identified that UC-MSCs promote a more robust host innate immune response; in contrast, adult-MSCs appear to facilitate remodeling of the extracellular matrix (ECM) with stronger activation of angiogenic cascades. These data should help facilitate the standardization of source-specific MSCs, such that their regenerative signatures can be confidently used to target specific disease processes.

Indexed as

Adult Stem CellsMesenchymal Stem CellsBone Marrow CellsGene Expression ProfilingHumansInfant, NewbornProteomeTranscriptomeProteomeimmunomodulatoryintegrativemesenchymal stem cellmulti-omicsproteomicssecretometranscriptomics

Identifiers

PMID36493779
PMCPMC9860079
OpenAlexW4311023687

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.