Phenotypiccelltransitionsareintegraltotissuedevelopment,regeneration,andhomeostasis, whereasinglepotentcelltypecangiverisetomorethanonefunctionallydistinctcelltype.To better understand the dynamics of cell fate decisions, a lineage topology, from common progenitortoeachdifferentiatedcellstate,mustbeknown.Whilesignalingprofilesofcellular end states, stem and differentiated states, are generally well characterized, the signaling dynamics along each trajectory and at branch points, splits in a common lineage, are less defined. Recent computational approaches have focused on defining stemdifferentiated cell transitions from single cell data. Defining the temporal progression of cell transitions is challengingduetothecontinuumofcellstatesthatexistbetweenstablestates.Whileprevious efforts have successfully mapped linear differentiation lineages, no approach exists that can map branched differentiation lineages with statistically testable results. Our method borrows concepts from graph theory, where a spanning tree is constructed from a densitydependent down sampled set of datapoints. Lineage branches and cellular end states (stem cells and differentiatedcelltypes)areidentifiedbycloseness,agraphattribute.Theresultingtransition mapsarestatisticallyscoredbothglobally,ontheoveralltopology,andlocally,oneachbranch point, using a modified rootmean squared deviation algorithm. A final transitional map comprisesasetoftopscoringlineageswhichcanbevalidatedagainstthespatialtemporalcell progression of the crypt. Our hypothesis is that a cell makes its fate decision based upon a probabilisticdistributionconditionedonitscurrentstate,itsage,andthestateofitssurrounding neighbors.Fromhyperplexedimagingtechnology,wewillmodelprobabilisticcellfatedecisions usingaBayesianframeworktoconstructtopologiesofsignalingdependencies.Themammalian intestinalepitheliumisourmodelsystem,whereitsshortturnovertimenecessitatescontinuous regenerationanddifferentiationofepithelialcells.Onceestablished,ourmodelwillprovidean uniqueinsightintotheeffectofdiseaseonstemdifferentiatedcelltransitions,pavingthewayfor newsystemsbasedtherapeutics.
Cellulartransitionsfromlessspecializedtypestomorespecializedtypesareintegraltotissue development, regeneration, and homeostasis, where a single cell type can give rise to more thanonefunctionallydistinctcelltype.Whilesignalingprofilesoffunctionallydistinctcellstates, aregenerallywellcharacterized,thesignalingdynamicsatcriticalcellfatedecisionpointsare not. Our algorithmic approaches will provide insight into how cells make fate decisions and provide unique insight into the effect of disease on cell transitions, paving the way for new systemsbasedtherapeutics.
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