- by Morrissey, J., Monteiro, M., Betenbaugh, M., Kontoravdi, C.Metabolism reflects evolutionary priorities that govern how cells allocate resources. In mammalian cells, metabolic objectives are layered and context-dependent, making it difficult to pinpoint the priorities that underlie observed phenotypes. Here, we introduce ObjFind-M, an inverse optimization framework that infers reaction-level metabolic objectives in mammalian cells directly from fluxomic and metabolomic data. Using Chinese hamster ovary (CHO) cells as a data-rich mammalian cell system, ObjFind-M consistently identifies mitochondrial ATP synthase as the central metabolic driver, supported by key TCA cycle […]
- by Marinos, G., Moors, K. A., Ruehlemann, M. C., Waschina, S., Lieb, W., Franke, A., Laudes, M., Groussin, M., Poyet, M., Kaleta, C., Kadibalban, A. S.Microbiomes and their host environments form complex, interconnected ecosystems. The microbial species within a microbiome, on the one hand, compete for resources, while on the other hand, they exchange vital metabolites to support their survival. These interactions are influenced by the microbial genetic repertoire, environmental conditions, and the availability of nutrients. We developed EcoGS (http://www.github.com/KaletaLab/EcoGS), a metabolic modelling tool designed to predict ecological interactions between pairs of microbes. Applying EcoGS to the microbiomes of two distinct human cohorts revealed a […]
- by Tsabar, M., Sheppard, S., Sturtevant, S., Huang, Y., Genga, R., Magaletta, M., Kodrasov, M., Tran, E., Smith, K., Jung, J., Sullivan, M., Kurlovs, A., Savova, V., Stoycheva, D., Figueroa, B., Goehlsdorf, D., Grella, A., de Rinaldis, E., Gaglia, G.In the field drug development ML/AI methods are being applied to improve drug production speed, costs, and reliability. In allogenic NK cell therapy production, one of the biggest challenges is the inherent variability in the donors that provide the starting material for NK cell expansion. In this study we performed PM21-mediated NK cell expansion on 26 donors, and in parallel performed single-cell transcriptomics on the same donor sample prior to expansion. Canonical differential expression analysis and cell state abundance did […]
- by Grasemann, L., Han, J., Tischler, J., Arefi, F., Castillo, M. A. G., Irvine, E. B., Chen, N., Reddy, S. T., Maerkl, S. J.SARS-CoV-2 variants continue to threaten public health, necessitating the study of cumulative and epistatic effects of receptor-binding domain (RBD) mutations on antibody evasion. We present a high-throughput platform combining cell-free protein synthesis and microfluidics to quantify the affinity of a large number of RBD triplet mutants covering the evolutionary space between wild-type and Omicron against two therapeutic antibodies and one engineered binder. Using rapid in vitro gene assembly and cell-free synthesis, we expressed 518 RBD variants and obtained 31,740 quantitative […]
- by Mädler, S. C., Schmacke, N. A., Palma, A., Namsaraeva, A., Can, A. O., Varlamova, V., Heumos, L., Arunkumar, M., Wallmann, G., Hornung, V., Theis, F. J., Mann, M.Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich information on cellular architecture and are accessible at scale across biological systems, making them an ideal foundation for modeling cell behavior. However, a standardized image format does not exist at the single-cell level. Here we present scPortrait, an scverse software package for generation, storage, and application of single-cell image datasets. scPortrait reads, stitches and segments raw fields of view with out-of-core […]
- by Beheler-Amass, M., Jackson, C. A., Gresham, D., Bonneau, R.Gene Regulatory Networks (GRNs) are complex dynamical systems that modulate gene expression and drive transitions between phenotypic cell states. Determining these networks is crucial in understanding how gene dysregulation can lead to phenotypic variation and perturbation responses. We present a novel biophysically-motivated neural ordinary differential equation (ODE) model framework with a biologically interpretable deep learning architecture that leverages dynamic single-cell data: in-CAHOOTTS (gene regulatory network Inference with Context Aware Hybrid neural-ODEs on Transcriptional Time-series Systems). Our approach combines accurate prediction […]
- by Ravikumar, P., Ravindran, A., Raman, K.Microbial community assembly processes can help us understand ecology, evolution, and climatic influences on community composition while presenting opportunities for biotechnological applications and improved marine conservation. In this study, we have investigated species richness patterns, community assembly mechanisms, and interaction patterns of marine microbial communities by analysing 16S rRNA amplicon sequencing data from 4,611 samples collected from major ocean microbiome projects. Using neutral community models, the iCAMP framework, and co-occurrence network analyses, we showed that stochastic processes drive microbial community […]
- by LeBlanc, C. J., Agarwal, P., Demaray, J., Hu, G., Zintel, M., Lam, A., Castro Hernandez, J. E., Staller, M. V.Deep neural networks have improved the accuracy of many difficult prediction tasks in biology, but it remains challenging to interpret these networks and learn molecular mechanisms. Here, we address the interpretability challenges associated with predicting transcriptional activation domains from protein sequence. Activation domains, regions within transcription factors that drive gene expression, were traditionally difficult to predict due to their sequence diversity and poor conservation. Multiple deep neural networks can now accurately predict activation domains, but these predictors are difficult to […]
- by Chakrabarti, S., Makhmut, A., Mohammadi, A., Luo, W., Wang, L., Lewin, G. R., Coscia, F.The richness of our somatosensory experience is reflected in the functional diversity of somatic sensory neurons. Single-cell sequencing (scRNA) of sensory neurons has revealed a molecular basis for such diversity1-3. However, sensory neuron diversity has yet to be captured at the level of the proteome. Here, we combined electrophysiology with deep visual proteomics (DVP)4 to quantify over 6,000 proteins from phenotypically-defined sensory neurons and identified novel proteomic markers of sensory neuron subtypes. Comparative analysis revealed both concordance and meaningful divergence […]
- by Jiang, J., Chen, S., Park, J. H., Tsou, T., Yang, V., Rivaud, P., Thomson, M.During development, progenitors integrate external signaling cues to control differentiation. How combinatorial signal inputs modulate fate decisions and the underlying molecular information processing logic remains elusive. In this study, using single-cell mRNA-seq and regulatory network reconstruction, we identify an additive signal integration rule in mouse neural progenitor cells (NPCs) where the probability of neuronal versus glial cell fate choice is quantitatively regulated log-linearly by the input of EGF/FGF2, BMP4, and Wnt signaling. By profiling the developing mouse brain and NPCs […]
- by Arif, M., Doran, S., Clausen, M., Wikström, J., Bohlooly-Y, M., Bjönson, E., Davidsson, L., Jepsson, A., Levin, M., Mardinoglu, A., Boren, J.Ischemic heart disease (IHD) involves coordinated molecular changes across heart, yet their interplay remains poorly understood. Here, we investigated transcriptomic alterations in two heart tissue subtypes, left ventricle (LV) and epicardial adipose tissue (EAT), from age- and BMI-matched healthy and IHD individuals, including both diabetic and non-diabetic patients. We performed transcriptomic profiling and systems-level network analysis to identify disease-associated gene expression changes. Our analysis revealed: (1) stronger transcriptional responses in EAT compared to LV, particularly in diabetic individuals, and (2) […]
- by Leduc, A., Shipkovenska, G., Xu, Y., Franks, A., Slavov, N.Protein synthesis and clearance are major regulatory steps of gene expression, but their in vivo regulatory roles across the cells comprising complex tissues remains unexplored. Here, we systematically quantify protein synthesis and clearance across over 4,200 cells from a primary tissue. Through integration with single-cell transcriptomics, we report the first quantitative analysis of how individual cell types regulate their proteomes across the continuum of gene expression. Our analysis quantifies the relative contributions of RNA abundance, translation, and protein clearance to […]
- by Yuan, Y., Hefner, Y., Szubin, R., Sung, J., Palsson, B.The earliest responses of pathogenic bacteria to antibiotics can affect the outcome of an infection. While long-term adaptations have been extensively studied, the immediate transcriptional changes that unfold immediately following antibiotic exposure remain poorly understood. Here, we applied iModulon analysis to time-resolved transcriptomic data from Escherichia coli exposed to subinhibitory concentrations of two antibiotics (ampicillin and ciprofloxacin), capturing transcriptional regulatory changes occurring within the first 30 minutes of exposure. This analysis reveals an integrated, three-phase response: an immediate and sustained […]
- by Salla, M., Obermayer, B., Cotta, M., Friebel, E., Campo-Garcia, J., Charalambous, G., Bueno, R. J., Lieu, D., Dabek, P., Helmuth, A., Tellides, G., Assi, R., Bankov, K., Lodrini, M., Deubzer, H., Chung, H., Beule, D., Radbruch, H., Capper, D., Heppner, F., Starossom, S. C., Lareau, C., Liu, I., Ludwig, L. S.High-throughput clonal tracing of primary human samples relies on naturally occurring barcodes, such as somatic mitochondrial DNA (mtDNA) mutations detected via single-cell ATAC-seq (mtscATAC-seq). Fresh-frozen clinical specimens preserve tissue architecture but compromise cell integrity, thereby precluding their use in multi- omic approaches such as mitochondrial genotyping at single-cell resolution. Here, we introduce Cryo-mtscATAC-seq, a broadly applicable method for diverse pathophysiological contexts to isolate nuclei with their associated mitochondria ("CryoCells") from frozen samples for high-throughput clonal analysis. We applied Cryo-mtscATAC-seq to […]
- by Hoerter, A., Petrucciani, A., Marshed, F., Mwamzuka, M., Ahmed, A., Khaitan, A., Pienaar, E.We and others have reported evidence of T cell exhaustion in children with perinatal HIV with increased expression of inhibitory receptors PD-1, CD160, and TIM-3, but there is limited data on the virologic functional consequences of this immune exhaustion. We address this by using an immune database from Kenyan children with perinatal HIV and unexposed controls. We computationally integrate T cell profiles of differentiation, activation and exhaustion in an agent-based model (ABM) to predict how T cell exhaustion impacts viral […]
- by Morikura, T., Sakaguchi, K., Tanaka, R.-i., Iwasaki, K., Shimizu, T.To advance the industrialization of cultured meat and regenerative medicine, scalable and efficient cell culture techniques are essential. Among these, the suspension culture method using microcarriers has emerged as a promising approach for the large-scale cell culture technique. However, monitoring cell growth on the microcarriers remains challenging, particularly in developing cell counting techniques that can be seamlessly integrated into bioprocess workflows without cell detachment, fluorescence labeling and any parameter tuning in the analysis algorithm. In this study, we proposed a […]
- by Cakir, T., Abdik, E.Understanding the heterogeneous nature of Parkinsons disease is crucial for improving diagnostic and treatment strategies that benefit distinct patient subgroups. Genome-scale metabolic models, when integrated with omics data, provide powerful frameworks for such investigations. Here, we predicted patient-specific metabolite secretion patterns in the form of oversecretion/undersecretion by the TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions (TAMBOOR) algorithm. We first identified biomarkers for the general PD population using a consensus approach that prioritized changes consistent across the patient cohort. Then, we clustered […]
- by Kikuchi, Y., Asakura, Y., Aoki, K., Kondo, Y., Naoki, H.Collective cell migration is fundamental to tissue homeostasis and underlies biological processes such as wound healing and cancer invasion. Previous work has proposed governing equations to describe how chemical and mechanical inputs regulate these movements, but the quantitative validity of such models remains to be thoroughly assessed. Here, we developed a machine-learning framework that infers the governing equation from live-cell imaging data. Applied to epithelial sheet migration driven by MAPK/ERK, our approach quantitatively predicted single-cell movement from local chemical and […]
- by Berg, M. D., Chang, A. T., Rodriguez-Mias, R. A., Villen, J.Transfer RNAs (tRNAs) ensure accurate decoding of the genetic code. However, mutations in tRNAs can lead to mis-incorporation of an amino acid that differs from the genetic message in a process known as mistranslation. As mistranslating tRNAs modify how the genetic message is decoded, they have potential as therapeutic tools for diseases caused by nonsense and missense mutations. Despite this, they also produce proteome-wide mis-made proteins which can disrupt proteostasis. To better understand the impact of mistranslating tRNA variants, we […]
- by Nishi, K., Tero, A., Nishigami, Y., Nakagaki, T.Learning abilities, once thought to be unique to higher animals, have been reported to exist in their primitive form in single-celled organisms. This has triggered a growing interest in carefully examining the nature and mechanisms of the primitive versions of learning abilities, which would provide important clues for understanding the evolution of behavioral capabilities in organisms. In this study, we focused on previous experimental studies showing that the slime mold Physarum polycephalum, a model organism for studying protist behavior, exhibits […]
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