PXD047198-1

PXD047198 is an original dataset announced via ProteomeXchange.

Title	Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion
Description	Cells dynamically change their internal organization via continuous cell state transitions to mediate a plethora of physiological processes. Understanding such continuous processes is severely limited due to a lack of tools to measure the holistic physiological state of single cells undergoing a transition. We combined live-cell imaging and machine learning to quantitatively monitor skeletal muscle precursor cell (myoblast) differentiation during multinucleated muscle fiber formation. Our machine learning model predicted the continuous differentiation state of single primary murine myoblasts over time and revealed that inhibiting ERK1/2 leads to a gradual transition from an undifferentiated to a terminally differentiated state 7.5-14.5 hours post inhibition. Myoblast fusion occurred ~3 hours after predicted terminal differentiation. Moreover, we showed that our model could predict that cells have reached terminal differentiation under conditions where fusion was stalled, demonstrating potential applications in screening. This method can be adapted to other biological processes to reveal connections between the dynamic single-cell state and virtually any other functional readout.
HostingRepository	PRIDE
AnnounceDate	2024-05-23
AnnouncementXML	Submission_2024-05-23_00:01:09.378.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Tamar Ziv
SpeciesList	scientific name: Mus musculus (Mouse); NCBI TaxID: 10090;
ModificationList	iodoacetamide derivatized residue
Instrument	Q Exactive HF

Revision	Datetime	Status	ChangeLog Entry
0	2023-11-23 06:18:38	ID requested
⏵ 1	2024-05-23 00:01:10	announced
2	2024-10-22 06:42:11	announced	2024-10-22: Updated project metadata.

10.1038/s44320-024-00010-3;

Shakarchy A, Zarfati G, Hazak A, Mealem R, Huk K, Ziv T, Avinoam O, Zaritsky A, Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion. Mol Syst Biol, 20(3):217-241(2024) [pubmed]

submitter keyword: Machine learning, differentiation, myoblast

Ori Avinoam
contact affiliation	Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 761001, Israel
contact email	ori.avinoam@weizmann.ac.il
lab head
Tamar Ziv
contact affiliation	Technion
contact email	tamarz@technion.ac.il
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2024/05/PXD047198

PRIDE project URI

• PRIDE
  1. PXD047198
     1. Label: PRIDE project
     2. Name: Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion