19 research outputs found
scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data
With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses. For complete details on the use and execution of this protocol, please refer to Wagner et al. (2019)
Inference of protein kinetics by stochastic modeling and simulation of fluorescence recovery after photobleaching experiments
Motivation: Fluorescence recovery after photobleaching (FRAP) is a functional live cell imaging technique that permits the exploration of protein dynamics in living cells. To extract kinetic parameters from FRAP data, a number of analytical models have been developed. Simplifications are inherent in these models, which may lead to inexhaustive or inaccurate exploitation of the experimental data. An appealing alternative is offered by the simulation of biological processes in realistic environments at a particle level. However, inference of kinetic parameters using simulation-based models is still limited. Results: We introduce and demonstrate a new method for the inference of kinetic parameter values from FRAP data. A small number of in silico FRAP experiments is used to construct a mapping from FRAP recovery curves to the parameters of the underlying protein kinetics. Parameter estimates from experimental data can then be computed by applying the mapping to the observed recovery curves. A bootstrap process is used to investigate identifiability of the physical parameters and determine confidence regions for their estimates. Our method circumvents the computational burden of seeking the best-fitting parameters via iterative simulation. After validation on synthetic data, the method is applied to the analysis of the nuclear proteins Cdt1, PCNA and GFPnls. Parameter estimation results from several experimental samples are in accordance with previous findings, but also allow us to discuss identifiability issues as well as cell-to-cell variability of the protein kinetics. Implementation: All methods were implemented in MATLAB R2011b. Monte Carlo simulations were run on the HPC cluster Brutus of ETH Zurich. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin
Matching single cells across modalities with contrastive learning and optimal transport.
Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching
CK1δ restrains lipin-1 induction, lipid droplet formation and cell proliferation under hypoxia by reducing HIF-1α/ARNT complex formation.
Proliferation of cells under hypoxia is facilitated by metabolic adaptation, mediated by the transcriptional activator Hypoxia Inducible Factor-1 (HIF-1). HIF-1α, the inducible subunit of HIF-1 is regulated by oxygen as well as by oxygen-independent mechanisms involving phosphorylation. We have previously shown that CK1δ phosphorylates HIF-1α in its N-terminus and reduces its affinity for its heterodimerization partner ARNT. To investigate the importance of this mechanism for cell proliferation under hypoxia, we visually monitored HIF-1α interactions within the cell nucleus using the in situ proximity ligation assay (PLA) and fluorescence recovery after photobleaching (FRAP). Both methods show that CK1δ-dependent modification of HIF-1α impairs the formation of a chromatin binding HIF-1 complex. This is confirmed by analyzing expression of lipin-1, a direct target of HIF-1 that mediates hypoxic neutral lipid accumulation. Inhibition of CK1δ increases lipid droplet formation and proliferation of both cancer and normal cells specifically under hypoxia and in an HIF-1α- and lipin-1-dependent manner. These data reveal a novel role for CK1δ in regulating lipid metabolism and, through it, cell adaptation to low oxygen conditions.This work was supported by the “ARISTEIA ΙΙ” Action of the “OPERATIONAL PROGRAMME EDUCATION AND LIFELONG LEARNING” and was co-funded by the European Social Fund (ESF) and National Resources. Partial support was provided by the Proof of Concept Studies for the ESFRI project Euro-BioImaging (Greek BioImaging Facility, PCS facility Nr. 9, Unit 2). N.-N.G., M.A.R. and Z.L. were supported by a grant from the European Research Council and S.S. was supported by a Medical Research Council Senior Fellowship (grant number G0701446).This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0898656815000637
Running title: Maximal loading of MCM2/4 in late G1
Once-per-cell cycle replication is regulated through the assembly onto chromatin of multisubunit protein complexes that license DNA for a further round of replication. Licensing consists of the loading of the hexameric MCM2-7 complex onto chromatin during G1 phase and is dependent on the licensing factor Cdt1. In vitro experiments have suggested a two-step binding mode for minichromosome maintenance (MCM) proteins, with transient initial interactions converted to stable chromatin loading. Here, we assess MCM loading in live human cells using an in vivo licensing assay on the basis of fluorescence recovery after photobleaching of GFP-tagged MCM protein subunits through the cell cycle. We show that, in telophase, MCM2 and MCM4 maintain transient interactions with chromatin, exhibiting kinetics similar to Cdt1. These are converted to stable interactions from early G1 phase. The immobile fraction of MCM2 and MCM4 increases during G1 phase, suggestive of reiterative licensing. In late G1 phase, a large fraction of MCM proteins are loaded onto chromatin, with maximal licensing observed just prior to S phase onset. Fluorescence loss in photobleaching experiments show subnuclear concentrations of MCM-chromatin interactions that differ as G1 phase progresses and do not colocalize with sites of DNA synthesis in S phase.Fil: Symeonidou, Ioanna Eleni. University of Patras. School of Medicine. Laboratory of General Biology; Grecia;Fil: Kotsantis, Panagiotis. University of Patras. School of Medicine. Laboratory of General Biology; Grecia;Fil: Roukos, Vassilis. University of Patras. School of Medicine. Laboratory of General Biology; Grecia;Fil: Rapsomaniki, Maria Anna. University of Patras. School of Medicine. Laboratory of General Biology; Grecia;Fil: Grecco, Hernan Edgardo. Max Planck Institute of Molecular Physiology. Department of Systemic Cell Biology; Alemania; Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires; Argentina;Fil: Bastiaens, Philippe. Max Planck Institute of Molecular Physiology. Department of Systemic Cell Biology; Alemania;Fil: Taraviras, Stavros. University of Patras. School of Medicine. Laboratory of Physiology; Grecia;Fil: Lygerou, Zoi. University of Patras. School of Medicine. Laboratory of General Biology; Grecia
Applications of stochastic hybrid models in biological systems
Biological systems are inherently complex systems, consisting of a large number of biological entities that interact and cooperate in an orchestrated fashion in order to produce robust and adaptive behaviors. At the same time, biological systems are characterized by a high degree of randomness and uncertainty, as these interactions occur in a probabilistic manner. System-level analysis of biological processes requires advanced experimental methods and sophisticated computational models, so as to fully describe the nature of biological phenomena. In this context, stochastic hybrid systems, that can efficiently describe complex processes that include continuous evolution and switch-like activation of their parts in a probabilistic manner, have already found important applications in systems biology modeling and set the basis for further investigation. This thesis concerns the applications of stochastic hybrid systems in the modeling of biological processes. Focus is given in two major applications: analysis of protein kinetics using Fluorescence Recovery After Photobleaching (FRAP) experiments and analysis of DNA re-replication.In the first part of the thesis, a complete workflow for the analysis of FRAP experimental data is proposed. We initially emphasized on the first steps of the analysis and developed the software easyFRAP, that enables the extraction of quantitative parameters from experimental FRAP recovery curves. We then present a stochastic hybrid model of FRAP experiments, based on a stochastic description of protein diffusion and binding at a particle level within a defined geometry representing the cell nucleus. Last, to infer kinetic parameters from FRAP experimental data, a novel parameter identification method was designed and implemented. The key idea behind the proposed method is the a priori construction of a mapping from the space of parameters associated with the recovery curves to the space of parameters of the underlying molecule kinetics. In this way, the method circumvents the repeated simulation of the model in the search of new estimates and provides interpolation within the range of parameters of the simulated curves. After validating the method in silico, parameter inference using experimental data on the proteins Cdt1-GFP, PCNA-GFP and GFPnls confirmed existing knowledge on the kinetic behavior of the proteins and provided additional insight on the cell-to-cell variability and identifiability of kinetic parameters.In the second part of the thesis, stochastic hybrid modeling is applied to the case of DNA re-replication, developed by refining existing work on normal DNA replication. The model accurately portrays the interplay between discrete dynamics, associated with different origin states, continuous dynamics, associated with the movement of the replication forks, and stochasticity, associated with random firing and re-firing events. Using input data from experimentally determined origin locations and efficiencies for the case of the fission yeast genome, the model allows the simulation of re-replication along the complete genome and thus the exploration of re-replication kinetics genome-wide. Comparison of in silico data for different model variations and sensitivity analysis on the model inputs has permitted insight into the parameters affecting re-replication dynamics. By comparing the simulated vs. the experimental amplification profiles at a population level, we observe that overall the simulated data reproduce the experimental re-replication pattern on a whole-genome scale, validating our approach. Striking differences regard specific regions, possibly attributed to location-specific cellular mechanisms. Analysis of simulated data at a single-cell level permits a more detailed insight into DNA re-replication. Amplification levels of individual loci were found to be affected by intrinsic properties in terms of firing efficiency, in cis effects from adjacent loci and in trans effects from distant loci. Overall, our analysis showed that, although re-replication profiles at the population level are robust, at the single-cell level they are characterized by a high degree of heterogeneity and can deviate significantly from the mean, since re-replication can, with varying probability, occur anywhere in the genome and generate many diverse genotypes within a population. Based on these observations we conclude that cell-to-cell variability is inherent in re-replication and can lead to a high degree of genome plasticity.Τα βιολογικά συστήματα είναι εγγενώς πολύπλοκα συστήματα, που αποτελούνται από ένα μεγάλο αριθμό βιολογικών οντοτήτων που αλληλεπιδρούν και συνεργάζονται με συντονισμένο τρόπο έτσι ώστε να οδηγήσουν σε σταθερές και προσαρμοστικές συμπεριφορές. Ταυτόχρονα, τα βιολογικά συστήματα χαρακτηρίζονται από μεγάλο βαθμό τυχαιότητας και αβεβαιότητας, καθώς αυτές οι αλληλεπιδράσεις συμβαίνουν με πιθανοτικό τρόπο. Η συστημική ανάλυση των βιολογικών διεργασιών απαιτεί εξελιγμένες πειραματικές μεθόδους και προηγμένα υπολογιστικά μοντέλα έτσι ώστε να περιγράψει πλήρως τη φύση των βιολογικών φαινομένων. Σε αυτό το πλαίσιο, τα στοχαστικά υβριδικά συστήματα, ικανά να περιγράψουν αποδοτικά πολύπλοκες διεργασίες που εμπεριέχουν συνεχή εξέλιξη και διακριτή ενεργοποίηση των τμημάτων τους με πιθανοτικό τρόπο, έχουν ήδη βρει σημαντικές εφαρμογές στη μοντελοποίηση βιολογικών συστημάτων και έχουν θέσει τη βάση για περαιτέρω διερεύνηση. Η παρούσα διατριβή αφορά στις εφαρμογές των στοχαστικών υβριδικών συστημάτων στη μοντελοποίηση βιολογικών διεργασιών, με έμφαση σε δύο βασικές εφαρμογές: την ανάλυση της κινητικής των πρωτεϊνών χρησιμοποιώντας πειράματα επαναφοράς φθορισμού μετά απο φωτολεύκανση (Fluorescence Recovery After Photoblaching - FRAP) και την ανάλυση της επαναντιγραφής του DNA. Στο πρώτο μέρος της διατριβής προτείνεται μια πλήρης ροή εργασίας για την ανάλυση των πειραμάτων FRAP. Αρχικά δόθηκε έμφαση στα πρώτα βήματα της ανάλυσης και για το σκοπό αυτό αναπτύχθηκε το λογισμικό easyFRAP, που επιτρέπει την εξαγωγή ποσοτικών παραμέτρων από πειραματικές καμπύλες FRAP. Στη συνέχεια παρουσιάζεται ένα στοχαστικό υβριδικό μοντέλο πειραμάτων FRAP, βασισμένο στη στοχαστική περιγραφή της διάχυσης και της πρόσδεσης των πρωτεϊνών σε επίπεδο μορίου μέσα σε μια καθορισμένη τρισδιάστατη περιοχή που αναπαριστά τον πυρήνα του κυττάρου. Τέλος, παρουσιάζεται μια νέα μέθοδος εξαγωγής των παραμέτρων της κινητικής από πειραματικά δεδομένα FRAP. Η βασική ιδέα πίσω από την προτεινόμενη μέθοδο είναι η εκ των προτέρων κατασκευή μιας χαρτογράφησης από το χώρο των παραμέτρων που σχετίζονται με τις καμπύλες επανάκτησης φθορισμού στο χώρο των παραμέτρων της κινητικής. Με αυτόν τον τρόπο η μέθοδος παρακάμπτει την επαναλαμβανόμενη προσομοίωση του μοντέλου κατά την αναζήτηση νέων προβλέψεων. Μετά την επικύρωση της μεθόδου χρησιμοποιώντας δεδομένα από προσομοίωση, η εξαγωγή παραμέτρων χρησιμοποιώντας πειραματικά δεδομένα από τις πρωτεΐνες Cdt1, PCNA και GFPnls επιβεβαίωσε την υπάρχουσα γνώση για την κινητική συμπεριφορά τους και παρείχε επιπρόσθετες πληροφορίες σχετικά με την ετερογένεια της κινητικής από κύτταρο σε κύτταρο και τη δυνατότητα πρόβλεψης και ακρίβεια των κινητικών παραμέτρων τους.Στο δεύτερο μέρος της διατριβής, τα στοχαστικά υβριδικά μοντέλα χρησιμοποιούνται για τη μελέτη της επαναντιγραφής του DNA, επεκτείνοντας υπάρχουσα δουλειά για την περίπτωση της φυσιολογικής αντιγραφής. Το μοντέλο που αναπτύχθηκε αποτυπώνει με ακρίβεια την αλληλεπίδραση ανάμεσα στη διακριτή δυναμική, που σχετίζεται με διαφορετικές καταστάσεις των αφετηριών αντιγραφής, τη συνεχή δυναμική, που σχετίζεται με την κίνηση των διχάλων αντιγραφής, και την αβεβαιότητα, που σχετίζεται με τα τυχαία γεγονότα πυροδότησης και επαναπυροδότησης των αφετηριών. Χρησιμοποιώντας ως είσοδο πειραματικά δεδομένα από τον οργανισμό S.pombe, που περιέχουν θέσεις αφετηριών πάνω στο γονιδίωμα και τις αντίστοιχες αποδοτικότητές τους, το μοντέλο επιτρέπει την προσομοίωση της επαναντιγραφής και συνεπώς τη διερεύνηση της κινητικής της επαναντιγραφής κατά μήκος ολόκληρου το γονιδιώματος. Συγκρίνοντας δεδομένα από προσομοιώσεις για διαφορετικές υλοποιήσεις του μοντέλου και αναλύοντας την ευαισθησία του μοντέλου σε διαφορετικές τιμές των μεταβλητών εισόδου, εξάγουμε συμπεράσματα σχετικά με τις παραμέτρους που επηρεάζουν περισσότερο τη δυναμική της επαναντιγραφής. Συγκρίνοντας τα προφίλ επαναντιγραφής σε επίπεδο πληθυσμού με αντίστοιχα πειραματικά δεδομένα, παρατηρούμε ότι συνολικά οι προβλέψεις του μοντέλου αναπαράγουν τις πειραματικές παρατηρήσεις σε μεγάλο βαθμό κατά το μήκος ολόκληρου του γονιδιώματος, γεγονός που επιβεβαιώνει την προσέγγισή μας. Κάποιες σημαντικές αποκλίσεις παρατηρούνται σε μεμονωμένες περιοχές και πιθανώς οφείλονται σε ειδικούς τοπικούς κυτταρικούς μηχανισμούς. Η ανάλυση των δεδομένων σε επίπεδο κυττάρου επιτρέπει μια πιο λεπτομερή ματιά στη διαδικασία της επαναντιγραφής. Τα επίπεδα επαναντιγραφής μεμονωμένων περιοχών πάνω στο γονιδίωμα βρέθηκαν να επηρεάζονται από τις εγγενείς τους ιδιότητες, όπως η αποδοτικότητα της πυροδότησης, από in cis αλληλεπιδράσεις με γειτονικές περιοχές και από in trans αλληλεπιδράσεις με απομακρυσμένες περιοχές. Συνολικά η ανάλυσή μας έδειξε ότι, παρά το γεγονός ότι τα προφίλ επαναντιγραφής σε επίπεδο πληθυσμού είναι σταθερά, σε επίπεδο κυττάρου χαρακτηρίζονται από μεγάλο βαθμό ετερογένειας και αποκλίνουν σημαντικά από το μέσο όρο. Η επαναντιγραφή μπορεί, με διαφορετική πιθανότητα, να συμβεί οπουδήποτε πάνω στο γονιδίωμα και να οδηγήσει σε πολλούς διαφορετικούς γονότυπους σε έναν πληθυσμό. Βασισμένοι σε αυτές τις παρατηρήσεις, συμπεραίνουμε πως η ετερογένεια σε επίπεδο κυττάρου είναι εγγενές χαρακτηριστικό της επαναντιγραφής και οδηγεί σε μεγάλο βαθμό πλαστικότητας του γονιδιώματος
Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity
A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, especially at the protein level, is critical for tracking tumor evolution, and showing the presence of different phenotypical variants and their location with respect to tissue architecture. Although a variety of techniques is available for quantifying protein expression, the heterogeneity observed in the tissue is rarely addressed. The proposed method is validated in breast cancer fresh-frozen tissues derived from five patients. Protein expression is quantified on the tissue regions of interest (ROI) with a resolution of up to 100 μm in diameter. High heterogeneity values across the analyzed patients in proteins such as cytokeratin 7, β-actin and epidermal growth factor receptor (EGFR) using a Shannon entropy analysis are observed. Additionally, ROIs are clustered according to their expression levels, showing their location in the tissue section, and highlighting that similar phenotypical variants are not always located in neighboring regions. Interestingly, a patient with a phenotype related to increased aggressiveness of the tumor presents a unique protein expression pattern. In summary, a workflow for the localized extraction and protein analysis of regions of interest from frozen tissues, enabling the evaluation of tumor heterogeneity at the protein level is presented
scQUEST: Quantifying tumor ecosystem heterogeneity from mass or flow cytometry data
Summary: With mass and flow cytometry, millions of single-cell profiles with dozens of parameters can be measured to comprehensively characterize complex tumor ecosystems. Here, we present scQUEST, an open-source Python library for cell type identification and quantification of tumor ecosystem heterogeneity in patient cohorts. We provide a step-by-step protocol on the application of scQUEST on our previously generated human breast cancer single-cell atlas using mass cytometry and discuss how it can be adapted and extended for other datasets and analyses.For complete details on the use and execution of this protocol, please refer to Wagner et al. (2019). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics
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CK1δ restrains lipin-1 induction, lipid droplet formation and cell proliferation under hypoxia by reducing HIF-1α/ARNT complex formation.
Proliferation of cells under hypoxia is facilitated by metabolic adaptation, mediated by the transcriptional activator Hypoxia Inducible Factor-1 (HIF-1). HIF-1α, the inducible subunit of HIF-1 is regulated by oxygen as well as by oxygen-independent mechanisms involving phosphorylation. We have previously shown that CK1δ phosphorylates HIF-1α in its N-terminus and reduces its affinity for its heterodimerization partner ARNT. To investigate the importance of this mechanism for cell proliferation under hypoxia, we visually monitored HIF-1α interactions within the cell nucleus using the in situ proximity ligation assay (PLA) and fluorescence recovery after photobleaching (FRAP). Both methods show that CK1δ-dependent modification of HIF-1α impairs the formation of a chromatin binding HIF-1 complex. This is confirmed by analyzing expression of lipin-1, a direct target of HIF-1 that mediates hypoxic neutral lipid accumulation. Inhibition of CK1δ increases lipid droplet formation and proliferation of both cancer and normal cells specifically under hypoxia and in an HIF-1α- and lipin-1-dependent manner. These data reveal a novel role for CK1δ in regulating lipid metabolism and, through it, cell adaptation to low oxygen conditions.This work was supported by the “ARISTEIA ΙΙ” Action of the “OPERATIONAL PROGRAMME EDUCATION AND LIFELONG LEARNING” and was co-funded by the European Social Fund (ESF) and National Resources. Partial support was provided by the Proof of Concept Studies for the ESFRI project Euro-BioImaging (Greek BioImaging Facility, PCS facility Nr. 9, Unit 2). N.-N.G., M.A.R. and Z.L. were supported by a grant from the European Research Council and S.S. was supported by a Medical Research Council Senior Fellowship (grant number G0701446).This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0898656815000637
ScLinear predicts protein abundance at single-cell resolution
Abstract Single-cell multi-omics have transformed biomedical research and present exciting machine learning opportunities. We present scLinear, a linear regression-based approach that predicts single-cell protein abundance based on RNA expression. ScLinear is vastly more efficient than state-of-the-art methodologies, without compromising its accuracy. ScLinear is interpretable and accurately generalizes in unseen single-cell and spatial transcriptomics data. Importantly, we offer a critical view in using complex algorithms ignoring simpler, faster, and more efficient approaches