TY  - JOUR
AB  - The abundance of a protein is defined by its continuous synthesis and degradation, a process known as protein turnover. Here, we systematically profiled the turnover of proteins in influenza A virus (IAV)-infected cells using a pulse-chase stable isotope labeling by amino acids in cell culture (SILAC)-based approach combined with downstream statistical modeling. We identified 1,798 virus-affected proteins with turnover changes (tVAPs) out of 7,739 detected proteins (data available at pulsechase.innatelab.org). In particular, the affected proteins were involved in RNA transcription, splicing and nuclear transport, protein translation and stability, and energy metabolism. Many tVAPs appeared to be known IAV-interacting proteins that regulate virus propagation, such as KPNA6, PPP6C, and POLR2A. Notably, our analysis identified additional IAV host and restriction factors, such as the splicing factor GPKOW, that exhibit significant turnover rate changes while their total abundance is minimally affected. Overall, we show that protein turnover is a critical factor both for virus replication and antiviral defense.
AU  - Huang, Y.*
AU  - Urban, C.*
AU  - Hubel, P.*
AU  - Stukalov, A.*
AU  - Pichlmair, A.
C1  - 71929
C2  - 56489
SP  - 911-929
TI  - Protein turnover regulation is critical for influenza A virus infection.
JO  - Cell Syst.
VL  - 15
IS  - 10
PY  - 2024
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Long non-coding RNAs (lncRNAs) are involved in gene expression regulation in cis. Although enriched in the cell chromatin fraction, to what degree this defines their regulatory potential remains unclear. Furthermore, the factors underlying lncRNA chromatin tethering, as well as the molecular basis of efficient lncRNA chromatin dissociation and its impact on enhancer activity and target gene expression, remain to be resolved. Here, we developed chrTT-seq, which combines the pulse-chase metabolic labeling of nascent RNA with chromatin fractionation and transient transcriptome sequencing to follow nascent RNA transcripts from their transcription on chromatin to release and allows the quantification of dissociation dynamics. By incorporating genomic, transcriptomic, and epigenetic metrics, as well as RNA-binding protein propensities, in machine learning models, we identify features that define transcript groups of different chromatin dissociation dynamics. Notably, lncRNAs transcribed from enhancers display reduced chromatin retention, suggesting that, in addition to splicing, their chromatin dissociation may shape enhancer activity.
AU  - Ntini, E.*
AU  - Budach, S.*
AU  - Vang Ørom, U.A.*
AU  - Marsico, A.
C1  - 68717
C2  - 54926
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 906-922.e6
TI  - Genome-wide measurement of RNA dissociation from chromatin classifies transcripts by their dynamics and reveals rapid dissociation of enhancer lncRNAs.
JO  - Cell Syst.
VL  - 14
IS  - 10
PB  - Cell Press
PY  - 2023
SN  - 2405-4712
ER  - 

TY  - JOUR
AU  - Theis, F.J.
AU  - Dar, D.*
AU  - Vento-Tormo, R.*
AU  - Vicković, S.*
AU  - Wang, L.*
AU  - Kagohara, L.T.*
AU  - Rendeiro, A.F.*
AU  - Joyce, J.A.*
C1  - 70203
C2  - 55059
SP  - 423-427
TI  - What do you most hope spatial molecular profiling will help us understand? Part 1.
JO  - Cell Syst.
VL  - 14
IS  - 6
PY  - 2023
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.
AU  - Boe, R.H.*
AU  - Ayyappan, V.*
AU  - Schuh, L.
AU  - Raj, A.*
C1  - 66845
C2  - 53317
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 1016-1032.e6
TI  - Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks.
JO  - Cell Syst.
VL  - 13
IS  - 12
PB  - Cell Press
PY  - 2022
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Knowing cell-type proportions in a tissue is very important to identify which cells or cell types are targeted by a disease or perturbation. Hence, several deconvolution methods have been proposed to infer cell-type proportions from bulk RNA samples. Their performance with noisy reference profiles and closely correlated cell types highly depends on the set of genes undergoing deconvolution. In this work, we introduce AutoGeneS, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples. AutoGeneS requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types. AutoGeneS can be applied to reference profiles from various sources like single-cell experiments or sorted cell populations. Ground truth cell proportions analyzed by flow cytometry confirmed the accuracy of AutoGeneS in identifying cell-type proportions. AutoGeneS is available for use via a standalone Python package (https://github.com/theislab/AutoGeneS).
AU  - Aliee, H.
AU  - Theis, F.J.
C1  - 62614
C2  - 50917
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 706-715.e4
TI  - AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution.
JO  - Cell Syst.
VL  - 12
IS  - 7
PB  - Cell Press
PY  - 2021
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
AU  - Ji, Y.
AU  - Lotfollahi, M.
AU  - Wolf, F.A.
AU  - Theis, F.J.
C1  - 62329
C2  - 50774
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 522-537
TI  - Machine learning for perturbational single-cell omics.
JO  - Cell Syst.
VL  - 12
IS  - 6
PB  - Cell Press
PY  - 2021
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - H4K20me kinetics in normal and cell-cycle-arrested Xenopus embryos. This quantitative model invokes specific methylation and unspecific demethylation and correctly predicts cell-cycle durations and cell-cycle dependencies. Active demethylation is not required to explain H4K20me kinetics of cycling cells, suggesting that overall H4K20me dilution through DNA replication is dominant. So only once cells stop cycling during embryogenesis, active H4K20 demethylation may contribute to shape histone methylation.
AU  - Schuh, L.
AU  - Loos, C.
AU  - Pokrovsky, D.*
AU  - Imhof, A.*
AU  - Rupp, R.A.W.*
AU  - Marr, C.
C1  - 60834
C2  - 49700
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 653-662.e8
TI  - H4K20 methylation is differently regulated by dilution and demethylation in proliferating and cell-cycle-arrested xenopus embryos.
JO  - Cell Syst.
VL  - 11
IS  - 6
PB  - Cell Press
PY  - 2020
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Non-genetic transcriptional variability is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes and survive therapy. How might these rare states arise and disappear within the population? It is unclear whether the canonical models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified mechanisms. We show that a minimal model of transcriptional bursting and gene interactions can give rise to rare coordinated high expression states, These states occur more frequently in networks with low connectivity and depend on three parameters. While entry into these states is initiated by a long transcriptional burst that also triggers entry of other genes, the exit occurs through independent inactivation of individual genes. Together, we demonstrate that established principles of gene regulation are sufficient to describe this behavior and argue for its more general existence. A record of this paper's transparent peer review process is included in the Supplemental Information.
AU  - Schuh, L.
AU  - Saint-Antoine, M.*
AU  - Sanford, E.M.*
AU  - Emert, B.L.*
AU  - Singh, A.*
AU  - Marr, C.
AU  - Raj, A.*
AU  - Goyal, Y.*
C1  - 58895
C2  - 48559
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 363-378.e12
TI  - Gene networks with transcriptional bursting recapitulate rare transient coordinated high expression states in cancer.
JO  - Cell Syst.
VL  - 10
IS  - 4
PB  - Cell Press
PY  - 2020
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 10 4 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.
AU  - Fröhlich, F.
AU  - Kessler, T.*
AU  - Weindl, D.
AU  - Shadrin, A.*
AU  - Schmiester, L.
AU  - Hache, H.*
AU  - Muradyan, A.*
AU  - Schütte, M.*
AU  - Lim, J.H.*
AU  - Heinig, M.
AU  - Theis, F.J.
AU  - Lehrach, H.*
AU  - Wierling, C.*
AU  - Lange, B.*
AU  - Hasenauer, J.
C1  - 54913
C2  - 45943
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 567-579
TI  - Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model.
JO  - Cell Syst.
VL  - 7
IS  - 6
PB  - Cell Press
PY  - 2018
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - All biological systems exhibit cell-to-cell variability. Frameworks exist for understanding how stochastic fluctuations and transient differences in cell state contribute to experimentally observable variations in cellular responses. However, current methods do not allow identification of the sources of variability between and within stable subpopulations of cells. We present a data-driven modeling framework for the analysis of populations comprising heterogeneous subpopulations. Our approach combines mixture modeling with frameworks for distribution approximation, facilitating the integration of multiple single-cell datasets and the detection of causal differences between and within subpopulations. The computational efficiency of our framework allows hundreds of competing hypotheses to be compared. We initially validate our method using simulated data with an understood ground truth, then we analyze data collected using quantitative single-cell microscopy of cultured sensory neurons involved in pain initiation. This approach allows us to quantify the relative contribution of neuronal subpopulations, culture conditions, and expression levels of signaling proteins to the observed cell-to-cell variability in NGF/TrkA-initiated Erk1/2 signaling. Loos et al. introduce a data-driven modeling framework for the mechanistic analysis of heterogeneous cell populations consisting of subpopulations. Applying the framework to single-cell microscopy data of primary sensory neurons, they analyze the influence of extracellular scaffolds onto sensitization signaling.
AU  - Loos, C.
AU  - Möller, K.*
AU  - Fröhlich, F.
AU  - Hucho, T.*
AU  - Hasenauer, J.
C1  - 53404
C2  - 44582
SP  - 593-603.e13
TI  - A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability.
JO  - Cell Syst.
VL  - 6
IS  - 5
PY  - 2018
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Many proteins exhibit dynamic activation patterns in the form of irregular pulses. Such behavior is typically attributed to a combination of positive and negative feedback loops in the underlying regulatory network. However, the presence of positive feedbacks is difficult to demonstrate unequivocally, raising the question of whether stochastic pulses can arise from negative feedback only. Here, we use the protein kinase A (PKA) system, a key regulator of the yeast pulsatile transcription factor Msn2, as a case example to show that irregular pulses of protein activity can arise from a negative feedback loop alone. Simplification to two variables reveals that a combination of zero-order ultrasensitivity, timescale separation between the activator and the repressor, and an effective delay in the feedback are sufficient to amplify a perturbation into a pulse. The same circuit topology can account for both activation and inactivation pulses, pointing toward a general mechanism of stochastic pulse generation.
AU  - Martinez-Corral, R.*
AU  - Raimundez-Alvarez, E.
AU  - Lin, Y.*
AU  - Elowitz, M.B.*
AU  - Garcia-Ojalvo, J.*
C1  - 54520
C2  - 45626
CY  - 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
SP  - 453-462
TI  - Self-amplifying pulsatile protein dynamics without positive feedback.
JO  - Cell Syst.
VL  - 7
IS  - 4
PB  - Cell Press
PY  - 2018
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Mechanistic understanding of multi-scale biological processes, such as cell proliferation in a changing biological tissue, is readily facilitated by computational models. While tools exist to construct and simulate multi-scale models, the statistical inference of the unknown model parameters remains an open problem. Here, we present and benchmark a parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm, tailored for high-performance computing clusters. pABC SMC is fully automated and returns reliable parameter estimates and confidence intervals. By running the pABC SMC algorithm for ∼106 hr, we parameterize multi-scale models that accurately describe quantitative growth curves and histological data obtained in vivo from individual tumor spheroid growth in media droplets. The models capture the hybrid deterministic-stochastic behaviors of 105-106 of cells growing in a 3D dynamically changing nutrient environment. The pABC SMC algorithm reliably converges to a consistent set of parameters. Our study demonstrates a proof of principle for robust, data-driven modeling of multi-scale biological systems and the feasibility of multi-scale model parameterization through statistical inference. A new parallel approximate Bayesian computation sequential Monte Carlo (pABC SMC) algorithm allows for robust, data-driven modeling of multi-scale biological systems and demonstrates the feasibility of multi-scale model parameterization through statistical inference.
AU  - Jagiella, N.
AU  - Rickert, D.
AU  - Theis, F.J.
AU  - Hasenauer, J.
C1  - 50343
C2  - 42114
CY  - Cambridge
SP  - 194–206.e9
TI  - Parallelization and high-performance computing enables automated statistical inference of multi-scale models.
JO  - Cell Syst.
VL  - 4
IS  - 2
PB  - Cell Press
PY  - 2017
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Post-translational modifications (PTMs) are pivotal to cellular information processing, but how combinatorial PTM patterns (“motifs”) are set remains elusive. We develop a computational framework, which we provide as open source code, to investigate the design principles generating the combinatorial acetylation patterns on histone H4 in Drosophila melanogaster. We find that models assuming purely unspecific or lysine site-specific acetylation rates were insufficient to explain the experimentally determined motif abundances. Rather, these abundances were best described by an ensemble of models with acetylation rates that were specific to motifs. The model ensemble converged upon four acetylation pathways; we validated three of these using independent data from a systematic enzyme depletion study. Our findings suggest that histone acetylation patterns originate through specific pathways involving motif-specific acetylation activity.
AU  - Blasi, T.
AU  - Feller, C.*
AU  - Feigelman, J.
AU  - Hasenauer, J.
AU  - Imhof, A.*
AU  - Theis, F.J.
AU  - Becker, P.B.*
AU  - Marr, C.
C1  - 47793
C2  - 39469
SP  - 49-58
TI  - Combinatorial histone acetylation patterns are generated by motif-specific reactions.
JO  - Cell Syst.
VL  - 2
IS  - 1
PY  - 2016
SN  - 2405-4712
ER  - 

TY  - JOUR
AU  - Carpenter, A.*
AU  - Eddy, S.E.*
AU  - Flicek, P.*
AU  - Gymrek, M.*
AU  - Hammell, M.*
AU  - Jaqaman, K.*
AU  - Jenkins, J.*
AU  - Koller, D.*
AU  - Lappalainen, T.*
AU  - Oshlack, A.*
AU  - Shamir, R.*
AU  - Singh, M.*
AU  - Teichmann, S.*
AU  - Theis, F.J.
AU  - Troyanskaya, O.*
C1  - 49294
C2  - 41719
SP  - 7-11
TI  - What is the key best practice for collaborating with a computational biologist?
JO  - Cell Syst.
VL  - 3
IS  - 1
PY  - 2016
SN  - 2405-4712
ER  - 

TY  - JOUR
AB  - Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data.
AU  - Feigelman, J.
AU  - Ganscha, S.
AU  - Hastreiter, S.*
AU  - Schwarzfischer, M.
AU  - Filipczyk, A.*
AU  - Schröder, T.*
AU  - Theis, F.J.
AU  - Marr, C.
AU  - Claassen, M.
C1  - 50070
C2  - 41996
CY  - Cambridge
SP  - 480-490
TI  - Analysis of cell lineage trees by exact bayesian inference identifies negative autoregulation of nanog in mouse embryonic stem cells.
JO  - Cell Syst.
VL  - 3
IS  - 5
PB  - Cell Press
PY  - 2016
SN  - 2405-4712
ER  -