Nature, nurture, or chance: stochastic gene expression and its consequences

Gene expression and agent-based modeling improve precision prognosis in breast cancer

Breast cancer survival is hard to predict because of the complex ways genes and cells interact. This study offers a new method to improve these predictions by combining gene expression profiling (GEP) with agent-based modeling (ABM). First, GEP will pinpoint genes that are important in breast cancer development. Then, a mathematical model will be built to show how these genes influence cell behavior. This data will be used in ABM to simulate tumor growth and treatment response. The ABM allows us to virtually test different treatments and see how they might affect patient survival. Finally, the model's accuracy will be checked against real patient data and compared to other models. By combining the strengths of GEP and ABM, this research could significantly improve breast cancer survival prediction. ABM's ability to analyze interactions mathematically could pave the way for more personalized and effective treatments.

Genetically Encoded Fluorescence Barcodes Allow for Single-Cell Analysis via Spectral Flow Cytometry

Genetically encoded, single-cell barcodes are broadly useful for experimental tasks such as lineage tracing or genetic screens. For such applications, a barcode library would ideally have high diversity (many unique barcodes), nondestructive identification (repeated measurements in the same cells or population), and fast, inexpensive readout (many cells and conditions). Current nucleic acid barcoding methods generate high diversity but require destructive and slow/expensive readout, and current fluorescence barcoding methods are nondestructive, fast, and inexpensive to readout but lack high diversity. We recently proposed a theory for how fluorescent protein combinations may generate a high-diversity barcode library with nondestructive, fast, and inexpensive identification. Here, we present an initial experimental proof-of-concept by generating a library of ∼150 barcodes from two-way combinations of 18 fluorescent proteins, 61 of which are tested experimentally. We use a pooled cloning strategy to generate a barcode library that is validated to contain every possible combination of the 18 fluorescent proteins. Experimental results using single mammalian cells and spectral flow cytometry demonstrate excellent classification performance of individual fluorescent proteins, with the exception of mTFP1, and of most evaluated barcodes, with many true positive rates >99%. The library is compatible with genetic screening for hundreds of genes (or gene pairs) and lineage tracing hundreds of clones. This work lays a foundation for greater diversity libraries (potentially ∼105 and more) generated from hundreds of spectrally resolvable tandem fluorescent protein probes.

Inheritance of extraordinary metabolic activity from parental bacteria individuals

Many phenotypic traits, such as fermentation activity, have been shown to be instable due to stochastic gene expression and environmental influence. While previous studies only have obtained understanding at the level of the microbial community, the fate of extraordinary traits of an individual through generations of reproduction has yet to be adequately investigated. This work uses the lactic acid bacteri Lactiplantibacillus plantarum as a research model to study the activity inheritance between parental generations and filial generations. An integrated single-cell manipulation strategy is established, including fluorescent screening using an extracellular pH probe and a microwell array, micropicking using a micropipette, and amplifying an individual bacterium via single-cell culture. Consequently, it is found that daughter bacteria can well inherit the strong acid-producing activity from their parental bacterial individuals, although as the reproduction proceeds over 30 generations, the offspring gradually regresses to the mediocre, thus setting a caveat for the limiting generations for desired inheritance. This is likely due to the deterioration of the cell living environment. This work illustrates the inheritable features of bacterial metabolic traits at the level of individual bacteria and is therefore fundamentally insightful for biotechnological applications like bioenergy production that require consistent or at least predictable metabolic performance.

Maternal diabetes disrupts early corticogenesis through altered mitotic gene regulation: a transcriptomic analysis

Maternal diabetes is linked to neurodevelopmental impairments in offspring, but the underlying molecular mechanisms remain unclear. Early cortical neurogenesis is a critical window vulnerable to maternal metabolic disturbances. Here, we analyzed global gene expression by RNA sequencing in dorsal prosencephalon tissue from 12-day-old embryos without neural tube defects. Gene ontology (GO) enrichment identified key candidates, validated by qRT-PCR, Western blotting, and immunofluorescence. We found 247 differentially expressed genes (111 upregulated, 136 downregulated), with upregulated genes enriched in mitosis, microtubule organization, and chromosome segregation pathways. Aurkb and Numa1 emerged as central regulators and were confirmed upregulated by qRT-PCR. Although Western blotting showed no protein-level changes, immunofluorescence revealed altered subcellular localization, disrupted spindle architecture, monopolar spindles, and increased asymmetric divisions in neural stem cells. These results suggest maternal diabetes disrupts mitotic regulation, accelerates neurogenic differentiation, and depletes the neural stem cell pool, potentially contributing to cortical defects and neurodevelopmental impairments in offspring. This study provides new insight into the developmental origins of neurodevelopmental disorders in the context of maternal diabetes, highlighting mitotic dysregulation as a potential mechanistic link in fetal programming.

Activity Leads to Topological Phase Transition in 2D Populations of Heterogeneous Oscillators

Populations of heterogeneous, noisy oscillators on a two-dimensional lattice display short-range order. Here, we show that if the oscillators are allowed to actively move in space, the system undergoes instead a Berezenskii-Kosterlitz-Thouless transition and exhibits quasi-long-range order. This fundamental result connects two paradigmatic models-the XY and Kuramoto models-and provides insight into the emergence of order in active systems.

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, cell cycle phase, area and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.

Microcapillary Array-Based High Throughput Screening for Protein Biomanufacturability

Gene expression is a complex phenomenon involving numerous interlinked variables, and studying these variables to control expression is essential in bioengineering and biomanufacturing. While cloning techniques for achieving plasmid libraries that cover large design spaces exist, multiplex techniques offering cell culture screening at similar scales are still lacking. We introduced a microcapillary array-based platform aimed at high-throughput, multiplex screening of miniature cell cultures through fluorescent reporters. The clone recovery mechanism provides 100× enrichment ratios compared to traditional techniques for establishing phenotype-to-genotype linkages. We conducted experiments to delineate the effects of three key plasmid design features─promoters, 5' untranslated regions, and amino acid sequences─on protein titer. We identified a small set of promoters that maximize protein titer from thousands of promoters with widely varying transcription rates. We established that mRNA half-lives, controlled by 5' untranslated regions, correlate with protein expression. Using dual-reporter imaging, we demonstrate relative analyses of multiple ribosome binding sites in operons. Lastly, we discuss the effect of structural protein hydrophobicity scores on their expression and cell growth profiles. Through multiple experiments with libraries of plasmid constructs, we demonstrate population binning, dual-reporter operon screening, chemical perturbation, and cell growth estimation using brightfield absorbance measurements with the platform.

Comparative single-cell analysis of transcriptional bursting reveals the role of genome organization in de novo transcript origination

Spermatogenesis is a key developmental process underlying the origination of newly evolved genes. However, rapid cell type-specific transcriptomic divergence of the Drosophila germline has posed a significant technical barrier for comparative single-cell RNA-sequencing studies. By quantifying a surprisingly strong correlation between species- and cell type-specific divergence in three closely related Drosophila species, we apply a statistical procedure to identify a core set of 198 genes that are highly predictive of cell type identity while remaining robust to species-specific differences that span over 25 to 30 My of evolution. We then utilize cell type classifications based on the 198-gene set to show how transcriptional divergence in cell type increases throughout spermatogenic developmental time. After validating these cross-species cell type classifications using RNA fluorescence in situ hybridization and imaging, we then investigate the influence of genome organization on the molecular evolution of spermatogenesis vis-a-vis transcriptional bursting. We first show altering transcriptional burst size contributes to premeiotic transcription and altering bursting frequency contributes to postmeiotic expression. We then report global differences in autosomal vs. X chromosomal transcription may arise in a developmental stage preceding full testis organogenesis by showing evolutionarily conserved decreases in X-linked transcription bursting kinetics in all examined somatic and germline cell types. Finally, we provide evidence supporting the cultivator model of de novo gene origination by demonstrating how the appearance of newly evolved testis-specific transcripts potentially provides short-range regulation of neighboring genes' transcriptional bursting properties during key stages of spermatogenesis.

Analysis of gene expression within individual cells reveals spatiotemporal patterns underlying Vibrio cholerae biofilm development

Bacteria commonly exist in multicellular, surface-attached communities called biofilms. Biofilms are central to ecology, medicine, and industry. The Vibrio cholerae pathogen forms biofilms from single founder cells that, via cell division, mature into three-dimensional structures with distinct, yet reproducible, regional architectures. To define mechanisms underlying biofilm developmental transitions, we establish a single-molecule fluorescence in situ hybridization (smFISH) approach that enables accurate quantitation of spatiotemporal gene-expression patterns in biofilms at cell-scale resolution. smFISH analyses of V. cholerae biofilm regulatory and structural genes demonstrate that, as biofilms mature, overall matrix gene expression decreases, and simultaneously, a pattern emerges in which matrix gene expression becomes largely confined to peripheral biofilm cells. Both quorum sensing and c-di-GMP-signaling are required to generate the proper temporal pattern of matrix gene expression. Quorum sensing signaling is uniform across the biofilm, and thus, c-di-GMP-signaling alone sets the regional matrix gene expression pattern. The smFISH strategy provides insight into mechanisms conferring particular fates to individual biofilm cells.

Impact of molecular regulation on plant oil synthesis

The synthesis of lipids in plants is essential for their growth and development, and it has wide-ranging applications in various fields, including diet and industry. In the majority of plants, the principal unsaturated fatty acids (UFAs) are three C18 varieties: oleic acid (18:1), linoleic acid (18:2), and α-linolenic acid (18:3). Despite the clear delineation of the principal biosynthetic pathways of fatty acids in plants, numerous unresolved issues persist. The regulation of transcription factors can significantly influence the rate of fatty acid synthesis in plants. Consequently, several transcription factors associated with oil synthesis have been identified in recent years, among which the WRINKLED1 (WRI1) and V-myb avian myeloblastosis viral oncogene homolog (MYB) transcription factors play central roles. This study will explain how plants make essential lipids, bring up many unanswered questions, and describe the regulatory network of many transcription factors involved in oil production, with a focus on recent progress in research related to WRI1 and MYB1. The aim is to provide insights for the biological cultivation of high-quality oilseed crops.

Colony pattern multistability emerges from a bistable switch

Microbial colony development hinges upon a myriad of factors, including mechanical, biochemical, and environmental niches, which collectively shape spatial patterns governed by intricate gene regulatory networks. The inherent complexity of this phenomenon necessitates innovative approaches to comprehend and compare the mechanisms driving pattern formation. Here, we unveil the multistability of bacterial colony patterns, where bacterial colony patterns can stabilize into multiple distinct types including ring-like patterns and sector-like patterns on hard agar, orchestrated by a simple synthetic bistable switch. Utilizing quantitative imaging and spatially resolved transcriptome approaches, we explore the deterministic process of a ring-like colony pattern formation from a single cell. This process is primarily driven by bifurcation events programmed by the gene regulatory network and microenvironmental cues. Additionally, we observe a noise-induced process amplified by the founder effect, leading to patterns of symmetry-break during range expansion. The degrees of asymmetry are profoundly influenced by the initial conditions of single progenitor cells during the nascent stages of colony development. These findings underscore how the process of range expansion enables individual cells, exposed to a uniform growth-promoting environment, to exhibit inherent capabilities in generating emergent, self-organized behavior.

Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditions

Interindividual variation in phenotypic penetrance and severity is found in many neurodevelopmental conditions, although the underlying mechanisms remain largely unresolved. Within individuals, homogeneous cell types (i.e., genetically identical and in similar environments) can differ in molecule abundance. Here, we investigate the hypothesis that neurodevelopmental conditions can drive increased variability in gene expression, not just differential gene expression. Leveraging independent single-cell and single-nucleus RNA sequencing datasets derived from human brain-relevant cell and tissue types, we identify a significant increase in gene expression variability driven by the autosomal aneuploidy trisomy 21 (T21) as well as autism-associated chromodomain helicase DNA binding protein 8 (CHD8) haploinsufficiency. Our analyses are consistent with a global and, in part, stochastic increase in variability, which is uncoupled from changes in transcript abundance. Highly variable genes tend to be cell-type specific with modest enrichment for repressive H3K27me3, while least variable genes are more likely to be constrained and associated with active histone marks. Our results indicate that human neurodevelopmental conditions can drive increased gene expression variability in brain cell types, with the potential to contribute to diverse phenotypic outcomes. These findings also provide a scaffold for understanding variability in disease, essential for deeper insights into genotype-phenotype relationships.

Incorporating spatial diffusion into models of bursty stochastic transcription

The dynamics of gene expression are stochastic and spatial at the molecular scale, with messenger RNA (mRNA) transcribed at specific nuclear locations and then transported to the nuclear boundary for export. Consequently, the spatial distributions of these molecules encode their underlying dynamics. While mechanistic models for molecular counts have revealed numerous insights into gene expression, they have largely neglected now-available subcellular spatial resolution down to individual molecules. Owing to the technical challenges inherent in spatial stochastic processes, tools for studying these subcellular spatial patterns are still limited. Here, we introduce a spatial stochastic model of nuclear mRNA with two-state (telegraph) transcriptional dynamics. Observations of the model can be concisely described as following a spatial Cox process driven by a stochastically switching partial differential equation. We derive analytical solutions for spatial and demographic moments and validate them with simulations. We show that the distribution of mRNA counts can be accurately approximated by a Poisson-beta distribution with tractable parameters, even with complex spatial dynamics. This observation allows for efficient parameter inference demonstrated on synthetic data. Altogether, our work adds progress towards a new frontier of subcellular spatial resolution in inferring the dynamics of gene expression from static snapshot data.

Gradient matching accelerates mixed-effects inference for biochemical networks

MOTIVATION

Single-cell time series data often exhibit significant variability within an isogenic cell population. When modeling intracellular processes, it is therefore more appropriate to infer parameter distributions that reflect this variability, rather than fitting the population average to obtain a single point estimate. The Global Two-Stage (GTS) approach for nonlinear mixed-effects (NLME) models is a simple and modular method commonly used for this purpose. However, this method is computationally intensive due to its repeated use of nonconvex optimization and numerical integration of the underlying system.

RESULTS

We propose the Gradient Matching GTS (GMGTS) method as an efficient alternative to GTS. Gradient matching offers an integration-free approach to parameter estimation that is particularly powerful for systems that are linear in the unknown parameters, such as biochemical networks modeled by mass action kinetics. By incorporating gradient matching into the GTS framework, we expand its capabilities through uncertainty propagation calculations and an iterative estimation scheme for partially observed systems. Comparisons between GMGTS and GTS across various inference setups show that our method significantly reduces computational demands, facilitating the application of complex NLME models in systems biology.

AVAILABILITY AND IMPLEMENTATION

A Matlab implementation of GMGTS is provided at https://github.com/yulanvanoppen/GMGTS (DOI: http://doi.org/10.5281/zenodo.14884457).

Gene syntaxes modulate gene expression and circuit behavior on plasmids

Achieving consistent and predictable gene expression from plasmids remains challenging. While much attention has focused on intra-genetic elements like promoters and ribosomal binding sites, the spatial arrangement of genes within plasmids-referred to as gene syntax-also plays a crucial role in shaping gene expression dynamics. This study addresses the largely overlooked impact of gene syntaxes on gene expression variability and accuracy. Utilizing a dual-fluorescent protein system, we systematically investigated how different gene orientations and orders affect expression profiles including mean levels, relative expression ratios, and cell-to-cell variations. We found that arbitrary gene placement on a plasmid can cause significantly different expression means and ratios. Genes aligned in the same direction as a plasmid's origin of replication (Ori) typically exhibit higher expression levels; adjacent genes in the divergent orientation tend to suppress each other's expression; altering gene order without changing orientation can yield varied expression. Despite unchanged total cell-to-cell variation across different syntaxes, gene syntaxes can also influence intrinsic and extrinsic noise. Interestingly, cell-to-cell variation appears to depend on the reporter proteins, with RFP consistently showing higher variation than GFP. Moreover, the effects of gene syntax can propagate to downstream circuits, strongly affecting the performance of incoherent feedforward loops and contributing to unpredictable outcomes in genetic networks. Our findings reveal that gene syntaxes on plasmids modulate gene expression and circuit behavior, providing valuable insights for the rational design of plasmids and genetic circuits.

Comparative single cell analysis of transcriptional bursting reveals the role of genome organization on de novo transcript origination

Spermatogenesis is a key developmental process underlying the origination of newly evolved genes. However, rapid cell type-specific transcriptomic divergence of the Drosophila germline has posed a significant technical barrier for comparative single-cell RNA-sequencing (scRNA-Seq) studies. By quantifying a surprisingly strong correlation between species- and cell type-specific divergence in three closely related Drosophila species, we apply a new statistical procedure to identify a core set of 198 genes that are highly predictive of cell type identity while remaining robust to species-specific differences that span over 25-30 million years of evolution. We then utilize cell type classifications based on the 198-gene set to show how transcriptional divergence in cell type increases throughout spermatogenic developmental time. After validating these cross-species cell type classifications using RNA fluorescence in situ hybridization (FISH) and imaging, we then investigate the influence of genome organization on the molecular evolution of spermatogenesis vis-a-vis transcriptional bursting. We first show altering transcriptional burst size contributes to pre-meiotic transcription and altering bursting frequency contributes to post-meiotic expression. We then report global differences in autosomal vs. X chromosomal transcription may arise in a developmental stage preceding full testis organogenesis by showing evolutionarily conserved decreases in X-linked transcription bursting kinetics in all examined somatic and germline cell types. Finally, we provide evidence supporting the cultivator model of de novo gene origination by demonstrating how the appearance of newly evolved testis-specific transcripts potentially provides short-range regulation of neighboring genes' transcriptional bursting properties during key stages of spermatogenesis.

Exploring the Single-Cell Dynamics of FOXM1 Under Cell Cycle Perturbations

The cell cycle is crucial for maintaining normal cellular functions and preventing replication errors. FOXM1, a key transcription factor, plays a pivotal role in regulating cell cycle progression and is implicated in various physiological and pathological processes, including cancers like liver, prostate, breast, lung and colon cancer. Despite previous research, our understanding of FOXM1 dynamics under different cell cycle perturbations and its connection to heterogeneous cell fate decisions remains limited. In this study, we investigated FOXM1 behaviour in individual cells exposed to various perturbagens. We found that different drugs induce diverse responses due to heterogeneous FOXM1 dynamics at the single-cell level. Single-cell analysis identified six distinct cellular phenotypes: on-time cytokinesis, cytokinesis delay, cell cycle delay, G1 arrest, G2 arrest and cell death, observed across different drug types and doses. Specifically, treatments with PLK1, CDK1, CDK1/2 and Aurora kinase inhibitors revealed varied FOXM1 dynamics leading to heterogeneous cellular outcomes. Our findings affirm that the dynamics of FOXM1 are essential in shaping cellular outcomes, influencing the signals that dictate responses to various stimuli. Our results gave insights into how FOXM1 dynamics contribute to cell cycle fate decisions, especially under different cell cycle perturbations.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (1) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct Escherichia coli promoters and (2) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

NF-κB signaling directs a program of transient amplifications at innate immune response genes

The cellular response to pathogens involves an intricate response directed by key innate immune signaling pathways which is characterized by cell-to-cell heterogeneity. How this heterogeneity is established and regulated remains unclear. We describe a program of transient site-specific gains (TSSG) producing extrachromosomal DNA (ecDNA) of immune-related genes in response to innate immune signaling. Activation of NF-κB drives TSSG of the interferon receptor gene cluster through inducible recruitment of the transcription factor RelA and the pre-replication complex member MCM2 to an epigenetically regulated TSSG control element. Targeted recruitment of RelA or p300 are sufficient to induce TSSG formation. RelA and MCM2 specify a program of TSSG for at least six and as many as 179 regions enriched in innate immune response genes. Identification of this program reveals regulated production of ecDNA as a mechanism of heterogeneity in the host response.

Microscopic origin of the spatial and temporal precision in biological systems

All living systems display remarkable spatial and temporal precision, despite operating in intrinsically fluctuating environments. It is even more surprising given that biological phenomena are regulated by multiple chemical reactions that are also random. Although the underlying molecular mechanisms of surprisingly high precision in biology remain not well understood, a novel theoretical picture that relies on the coupling of relevant stochastic processes has recently been proposed and applied to explain different phenomena. To illustrate this approach, in this review, we discuss two systems that exhibit precision control: spatial regulation in bacterial cell size and temporal regulation in the timing of cell lysis by λ bacteriophage. In cell-size regulation, it is argued that a balance between stochastic cell growth and cell division processes leads to a narrow distribution of cell sizes. In cell lysis, it is shown that precise timing is due to the coupling of holin protein accumulation and the breakage of the cellular membrane. The stochastic coupling framework also allows us to explicitly evaluate dynamic properties for both biological systems, eliminating the need to utilize the phenomenological concept of thresholds. Excellent agreement with experimental observations is observed, supporting the proposed theoretical ideas. These observations also suggest that the stochastic coupling method captures the important aspects of molecular mechanisms of precise cellular regulation, providing a powerful new tool for more advanced investigations of complex biological phenomena.

Uncovering dynamic transcriptional regulation of methanogenesis via single-cell imaging of archaeal gene expression

Archaeal methanogenesis is a dynamic process regulated by various cellular and environmental signals. However, understanding this regulation is technically challenging due to the difficulty of measuring gene expression dynamics in individual archaeal cells. Here, we develop a multi-round hybridization chain reaction (HCR)-assisted single-molecule fluorescence in situ hybridization (FISH) method to quantify the transcriptional dynamics of 12 genes involved in methanogenesis in individual cells of Methanococcoides orientis. Under optimal growth condition, most of these genes appear to be expressed in a temporal order matching metabolic reaction order. Interestingly, an important environmental factor, Fe(III), stimulates cellular methane production without upregulating methanogenic gene expression, likely through a Fenton-reaction-triggered mechanism. Through single-cell clustering and kinetic analyses, we associate these gene expression patterns to a dynamic mixture of distinct cellular states, potentially regulated by a set of shared factors. Our work provides a quantitative framework for uncovering the mechanisms of metabolic regulation in archaea.

Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens

Stochastic transcriptional bursting is a universal property of active genes. While different genes exhibit distinct bursting patterns, the molecular mechanisms that govern gene-specific stochastic bursting are largely unknown. We have developed a high-throughput-imaging-based screening strategy to identify cellular factors that determine the bursting patterns of native genes in human cells. We identify protein acetylation as a prominent effector of burst frequency and burst size acting via decreasing off-times and gene-specific changes in the on-time. These effects are not correlated with promoter acetylation. Instead, we demonstrate acetylation of the Integrator complex as a key determinant of gene bursting that alters Integrator interactions with transcription elongation and RNA processing factors but without affecting pausing. Our results suggest a prominent role for non-histone acetylation of a transcription cofactors as a mechanism for modulation of bursting via a far-downstream checkpoint.

Cell cycle-dependent regulation of CRISPR-Cas9 repetitive activation by anti-CRISPR and Cdt1 fusion in the CRISPRa system

CRISPR-Cas9 is a widely used genome-editing tool. We previously developed a method with improved homology-directed repair efficiency and reduced off-target effects by utilizing a fusion protein of AcrIIA4, a Cas9 inhibitor, and Cdt1, which accumulates in the G1 phase and activates Cas9 only in the S/G2 phase. However, it is unknown whether Cas9 inhibition by AcrIIA4 + Cdt1 occurs repeatedly in the G1 phase as the cell cycle progresses. In this study, we used the CRISPRa system to monitor changes in the interaction between Cas9 and AcrIIA4 + Cdt1 at single-cell resolution and in real time. Our findings are among the few examples of successful detection of fluctuating protein-protein interactions that oscillate over time.

How Can Spatial Transcriptomic Profiling Advance Our Understanding of Skin Diseases?

Spatial transcriptomic (ST) profiling is the mapping of gene expression within cell populations with preservation of positional context and represents an exciting new approach to develop our understanding of local and regional influences upon skin biology in health and disease. With the ability to probe from a few hundred transcripts to the entire transcriptome, multiple ST approaches are now widely available. In this paper, we review the ST field and discuss its application to dermatology. Its potential to advance our understanding of skin biology in health and disease is highlighted through the illustrative examples of 3 research areas: cutaneous aging, tumorigenesis, and psoriasis.

Single-cell RNA-seq reveals diverse molecular signatures associated with Methotrexate resistance in primary central nervous system lymphoma cells

PURPOSE

Methotrexate is one of the most essential single agents in patients with primary central nervous system lymphoma (PCNSL). However, 25-50% result in relapse with a poor prognosis. Therefore, studies on methotrexate resistance are warranted to explore salvage chemotherapy for recurrent PCNSL. Single-cell sequence analysis enables the characterization of novel cell types and provides a precise understanding of cancer biology.

METHODS

Single-cell sequence analysis of parental and methotrexate-resistant PCNSL cells was performed. We used a Weighted Gene Co-expression Network Analysis to identify groups of significantly connected genes.

RESULTS

We identified consensus modules in both the HKBML and TK datasets. HLA-DRβ1, HLA-DQβ1,and SNRPG were hub genes those detected in both datasets revealed by network analysis. Cyclosporine A was selected as the candidate drug for treating methotrexate-resistant cells.

CONCLUSION

The results of the present study characterized the methotrexate resistance-related signaling pathways in cultured PCNSL cells. Overall, these results may account for variations in treatment responses and lead potential novel therapeutic strategies for patients with PCNSL.

Metformin's Effects on Cognitive Function from a Biovariance Perspective: A Narrative Review

This study examines the effects of metformin on brain functions focusing on the variability of the results reported in the literature. While some studies suggest that metformin may have neuroprotective effects in diabetic patients, others report an insignificant impact of metformin on cognitive function, or even a negative effect. We propose that this inconsistency may be due to intrinsic cellular-level variability among individuals, which we term "biovariance". Biovariance persists even in demographically homogeneous samples due to complex and stochastic biological processes. Additionally, the complex metabolic actions of metformin, including its influence on neuroenergetics and neuronal survival, may produce different effects depending on individual metabolic characteristics.

The Genomic Code: the genome instantiates a generative model of the organism

How does the genome encode the form of the organism? What is the nature of this genomic code? Inspired by recent work in machine learning and neuroscience, we propose that the genome encodes a generative model of the organism. In this scheme, by analogy with variational autoencoders (VAEs), the genome comprises a connectionist network, embodying a compressed space of 'latent variables', with weights that get encoded by the learning algorithm of evolution and decoded through the processes of development. The generative model analogy accounts for the complex, distributed genetic architecture of most traits and the emergent robustness and evolvability of developmental processes, while also offering a conception that lends itself to formalization.

Priestia megaterium cells are primed for surviving lethal doses of antibiotics and chemical stress

Antibiotic resistant infections kill millions worldwide yearly. However, a key factor in recurrent infections is antibiotic persisters. Persisters are not inherently antibiotic-resistant but can withstand antibiotic exposure by entering a non-dividing state. This tolerance often results in prolonged antibiotic usage, increasing the likelihood of developing resistant strains. Here, we show the existence of "primed cells" in the Gram-positive bacterium Priestia megaterium, formerly known as Bacillus megaterium. These cells are pre-adapted to become persisters prior to lethal antibiotic stress. Remarkably, this prepared state is passed down through multiple generations via epigenetic memory, enhancing survival against antibiotics and other chemical stress. Previously, two distinct types of persisters were proposed: Type I and Type II, formed during stationary and log phases, respectively. However, our findings reveal that primed cells contribute to an increase in persisters during transition and stationary phases, with no evidence supporting distinct phenotypes between Type I and Type II persisters.

Biomolecular condensates in immune cell fate

Fate decisions during immune cell development require temporally precise changes in gene expression. Evidence suggests that the dynamic modulation of these changes is associated with the formation of diverse, membrane-less nucleoprotein assemblies that are termed biomolecular condensates. These condensates are thought to orchestrate fate-determining transcriptional and post-transcriptional processes by locally and transiently concentrating DNA or RNA molecules alongside their regulatory proteins. Findings have established a link between condensate formation and the gene regulatory networks that ensure the proper development of immune cells. Conversely, condensate dysregulation has been linked to impaired immune cell fates, including ageing and malignant transformation. This Review explores the putative mechanistic links between condensate assembly and the gene regulatory frameworks that govern normal and pathological development in the immune system.

Innate Immune Memory is Stimulus Specific

Innate immune memory (also termed trained immunity) is defined in part by its ability to cross-protect against heterologous pathogens, and can be generated by many different stimuli, suggesting a "universal" trained state. However, different stimuli could form distinct memories, leading to stimulus-specific trained responses. Here, we use primary human monocyte-derived macrophages to demonstrate phenotypic and epigenetic stimulus specificity of innate immune memory six days after initial exposure. Quantification of cytokine production with single-molecule RNA imaging demonstrates stimulus-specific patterns of response to restimulation at the single cell level. Differential licensing of inflammatory transcription factors is associated with encoding of specificities in chromatin. Trained cells show stronger responses to secondary stimuli that are more similar to the initial stimulus they experienced, suggesting a functional role for these stimulus-specific memories. Rather than activating a universal training state, our findings demonstrate that different stimuli impart specific memories that generate distinct training phenotypes in macrophages.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

Gene function revealed at the moment of stochastic gene silencing

Gene expression is a dynamic and stochastic process characterized by transcriptional bursting followed by periods of silence. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to measure transcriptional bursting and silencing at the individual cell level. In this study, we introduce the single-cell Stochastic Gene Silencing (scSGS) method, which leverages the natural variability in single-cell gene expression to decipher gene function. For a target gene g under investigation, scSGS classifies cells into transcriptionally active (g + ) and silenced (g-) samples. It then compares these cell samples to identify differentially expressed genes, referred to as SGS-responsive genes, which are used to infer the function of the target gene g. Analysis of real data demonstrates that scSGS can reveal regulatory relationships up- and downstream of target genes, circumventing the survivorship bias that often affects gene knockout and perturbation studies. scSGS thus offers an efficient approach for gene function prediction, with significant potential to reduce the use of genetically modified animals in gene function research.

Cell-scale gene-expression measurements in Vibrio cholerae biofilms reveal spatiotemporal patterns underlying development

Bacteria commonly exist in multicellular, surface-attached communities called biofilms. Biofilms are central to ecology, medicine, and industry. The Vibrio cholerae pathogen forms biofilms from single founder cells that, via cell division, mature into three-dimensional structures with distinct, yet reproducible, regional architectures. To define mechanisms underlying biofilm developmental transitions, we establish a single-molecule fluorescence in situ hybridization (smFISH) approach that enables accurate quantitation of spatiotemporal gene-expression patterns in biofilms at cell-scale resolution. smFISH analyses of V. cholerae biofilm regulatory and structural genes demonstrate that, as biofilms mature, overall matrix gene expression decreases, and simultaneously, a pattern emerges in which matrix gene expression becomes largely confined to peripheral biofilm cells. Both quorum sensing and c-di-GMP-signaling are required to generate the proper temporal pattern of matrix gene expression. Quorum sensing autoinducer levels are uniform across the biofilm, and thus, c-di-GMP-signaling alone sets the regional matrix gene expression pattern. The smFISH strategy provides insight into mechanisms conferring particular fates to individual biofilm cells.

The impact of phenotypic heterogeneity on fungal pathogenicity and drug resistance

Phenotypic heterogeneity in genetically clonal populations facilitates cellular adaptation to adverse environmental conditions while enabling a return to the basal physiological state. It also plays a crucial role in pathogenicity and the acquisition of drug resistance in unicellular organisms and cancer cells, yet the exact contributing factors remain elusive. In this review, we outline the current state of understanding concerning the contribution of phenotypic heterogeneity to fungal pathogenesis and antifungal drug resistance.

MONITTR allows real-time imaging of transcription and endogenous proteins in C. elegans

Maximizing cell survival under stress requires rapid and transient adjustments of RNA and protein synthesis. However, capturing these dynamic changes at both single-cell level and across an organism has been challenging. Here, we developed a system named MONITTR (MS2-embedded mCherry-based monitoring of transcription) for real-time simultaneous measurement of nascent transcripts and endogenous protein levels in C. elegans. Utilizing this system, we monitored the transcriptional bursting of fasting-induced genes and found that the epidermis responds to fasting by modulating the proportion of actively transcribing nuclei and transcriptional kinetics of individual alleles. Additionally, our findings revealed the essential roles of the transcription factors NHR-49 and HLH-30 in governing the transcriptional kinetics of fasting-induced genes under fasting. Furthermore, we tracked transcriptional dynamics during heat-shock response and ER unfolded protein response and observed rapid changes in the level of nascent transcripts under stress conditions. Collectively, our study provides a foundation for quantitatively investigating how animals spatiotemporally modulate transcription in various physiological and pathological conditions.

m6A/YTHDF2-mediated mRNA decay targets TGF-β signaling to suppress the quiescence acquisition of early postnatal mouse hippocampal NSCs

Quiescence acquisition of proliferating neural stem cells (NSCs) is required to establish the adult NSC pool. The underlying molecular mechanisms are not well understood. Here, we showed that conditional deletion of the m6A reader Ythdf2, which promotes mRNA decay, in proliferating NSCs in the early postnatal mouse hippocampus elevated quiescence acquisition in a cell-autonomous fashion with decreased neurogenesis. Multimodal profiling of m6A modification, YTHDF2 binding, and mRNA decay in hippocampal NSCs identified shared targets in multiple transforming growth factor β (TGF-β)-signaling-pathway components, including TGF-β ligands, maturation factors, receptors, transcription regulators, and signaling regulators. Functionally, Ythdf2 deletion led to TGF-β-signaling activation in NSCs, suppression of which rescued elevated quiescence acquisition of proliferating hippocampal NSCs. Our study reveals the dynamic nature and critical roles of mRNA decay in establishing the quiescent adult hippocampal NSC pool and uncovers a distinct mode of epitranscriptomic control via co-regulation of multiple components of the same signaling pathway.

Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population

Ribosomes are responsible for the synthesis of proteins, the major component of cellular biomass. Classical experiments have established a linear relationship between the fraction of resources invested in ribosomal proteins and the rate of balanced growth of a microbial population. Very little is known, however, about how the investment in ribosomes varies over individual cells in a population. We therefore extended the study of ribosomal resource allocation from populations to single cells, using a combination of time-lapse fluorescence microscopy and statistical inference. We found a large variability of ribosome concentrations and growth rates in conditions of balanced growth of the model bacterium Escherichia coli in a given medium, which cannot be accounted for by the population-level growth law. A large variability in the allocation of resources to ribosomes was also found during the transition of the bacteria from a poor to a rich growth medium. While some cells immediately adapt their ribosome synthesis rate to the new environment, others do so only gradually. Our results thus reveal a range of strategies for investing resources in the molecular machines at the heart of cellular self-replication. This raises the fundamental question whether the observed variability is an intrinsic consequence of the stochastic nature of the underlying biochemical processes or whether it improves the fitness of Escherichia coli in its natural environment.

Nuclear lipids in chromatin regulation: Biological roles, experimental approaches and existing challenges

Lipids are crucial for various cellular functions. Besides the storage of energy equivalents, these include forming membrane bilayers and serving as signaling molecules. While significant progress has been made in the comprehension of the molecular and cellular biology of lipids, their functions in the cell nucleus remain poorly understood. The main role of the eukaryotic cell nucleus is to provide an environment for the storage and regulation of chromatin which is a complex of DNA, histones, and associated proteins. Recent studies suggest that nuclear lipids play a role in chromatin regulation and epigenetics. Here, we discuss various experimental methods in lipid-chromatin research, including biophysical, structural, and cell biology approaches, pointing out their strengths and weaknesses. We take the view that nuclear lipids have a far more widespread impact on chromatin than is currently acknowledged. This gap in comprehension is mostly due to existing experimental challenges in the study of lipid-chromatin biology. Several new, interdisciplinary approaches are discussed that could aid in elucidating the roles of nuclear lipids in chromatin regulation and gene expression.

Mechanisms and implications of phenotypic switching in bacterial pathogens

Bacteria encounter various stressful conditions within a variety of dynamic environments, which they must overcome for survival. One way they achieve this is by developing phenotypic heterogeneity to introduce diversity within their population. Such distinct subpopulations can arise through endogenous fluctuations in regulatory components, wherein bacteria can express diverse phenotypes and switch between them, sometimes in a heritable and reversible manner. This switching may also lead to antigenic variation, enabling pathogenic bacteria to evade the host immune response. Therefore, phenotypic heterogeneity plays a significant role in microbial pathogenesis, immune evasion, antibiotic resistance, host niche tissue establishment, and environmental persistence. This heterogeneity can result from stochastic and responsive switches, as well as various genetic and epigenetic mechanisms. The development of phenotypic heterogeneity may create clonal populations that differ in their level of virulence, contribute to the formation of biofilms, and allow for antibiotic persistence within select morphological variants. This review delves into the current understanding of the molecular switching mechanisms underlying phenotypic heterogeneity, highlighting their roles in establishing infections caused by select bacterial pathogens.

Investigating the Impact of Host Factors on Vaccinia Virus Infection Through Single-Cell Analysis via Flow Cytometry

To investigate the impact of a host factor on viral replication, conventional techniques involve analyzing viral replication within a homogeneous cell population engineered to uniformly express the host factor. In this chapter, we present a method to explore the intricate dynamics between host factors and vaccinia virus (VACV) infection using flow cytometry, which enables the rapid, multi-parametric analysis of individual cells in solution. Cultured cells are transfected to express a host factor fused with a fluorescent protein, followed by infection with a VACV encoding a different fluorescent reporter. Subsequently, cells are washed, fixed, and filtered for flow cytometry analysis. The data are then analyzed with flow cytometry analysis software to evaluate the influence of the host factor on various infection parameters, including the stage of viral infection and viral replication level. This method allows for the precise investigation of proviral or antiviral factor expression in relation to viral replication within a heterogeneous population of cells.

Dormancy in the origin, evolution and persistence of life on Earth

Life has existed on Earth for most of the planet's history, yet major gaps and unresolved questions remain about how it first arose and persisted. Early Earth posed numerous challenges for life, including harsh and fluctuating environments. Today, many organisms cope with such conditions by entering a reversible state of reduced metabolic activity, a phenomenon known as dormancy. This process protects inactive individuals and minimizes the risk of extinction by preserving information that stabilizes life-system dynamics. Here, we develop a framework for understanding dormancy on early Earth, beginning with a primer on dormancy theory and its core criteria. We hypothesize that dormancy-like mechanisms acting on chemical precursors in a prebiotic world may have facilitated the origin of life. Drawing on evidence from phylogenetic reconstructions and the fossil record, we demonstrate that dormancy is prevalent across the tree of life and throughout deep time. These observations lead us to consider how dormancy might have shaped nascent living systems by buffering stochastic processes in small populations, protecting against large-scale planetary disturbances, aiding dispersal in patchy landscapes and facilitating adaptive radiations. Given that dormancy is a fundamental and easily evolved property on Earth, it is also likely to be a feature of life elsewhere in the universe.

Exact distributions of threshold crossing times of proteins under post-transcriptional regulation by small RNAs

The timings of several cellular events like cell lysis, cell division, or pore formation in endosomes are regulated by the time taken for the relevant proteins to cross a threshold in number or concentration. Since protein synthesis is stochastic, the threshold crossing time is a first passage problem. The exact distributions of these first passage processes have been obtained recently for unregulated and autoregulated genes. Many proteins are however regulated by post-transcriptional regulation, controlled by small noncoding RNAs (sRNAs). Certain mathematical models of gene expression with post-transcriptional sRNA regulation have been recently exactly mapped to models without sRNA regulation. Utilizing this mapping and the exact distributions, we calculate exact results on fluctuations (full distribution, all cumulants, and characteristic times) of protein threshold crossing times in the presence of sRNA regulation. We derive two interesting predictions from these exact results. We show that the size of the fluctuation of the threshold crossing times have a nonmonotonic U-shaped behavior as a function of the rates of binding and unbinding of the sRNA-mRNA complex. Thus there are optimal parameters that minimize noise. Furthermore, the fluctuations in models with sRNA regulation may be higher or lower compared to the model without regulation, depending on the mean protein burst size.

Network for knowledge Organization (NEKO): An AI knowledge mining workflow for synthetic biology research

Large language models (LLMs) can complete general scientific question-and-answer, yet they are constrained by their pretraining cut-off dates and lack the ability to provide specific, cited scientific knowledge. Here, we introduce Network for Knowledge Organization (NEKO), a workflow that uses LLM Qwen to extract knowledge through scientific literature text mining. When user inputs a keyword of interest, NEKO can generate knowledge graphs to link bioinformation entities and produce comprehensive summaries from PubMed search. NEKO significantly enhance LLM ability and has immediate applications in daily academic tasks such as education of young scientists, literature review, paper writing, experiment planning/troubleshooting, and new ideas/hypothesis generation. We exemplified this workflow's applicability through several case studies on yeast fermentation and cyanobacterial biorefinery. NEKO's output is more informative, specific, and actionable than GPT-4's zero-shot Q&A. NEKO offers flexible, lightweight local deployment options. NEKO democratizes artificial intelligence (AI) tools, making scientific foundation model more accessible to researchers without excessive computational power.

The HIV-1 Transcriptional Program: From Initiation to Elongation Control

A large body of work in the last four decades has revealed the key pillars of HIV-1 transcription control at the initiation and elongation steps. Here, I provide a recount of this collective knowledge starting with the genomic elements (DNA and nascent TAR RNA stem-loop) and transcription factors (cellular and the viral transactivator Tat), and later transitioning to the assembly and regulation of transcription initiation and elongation complexes, and the role of chromatin structure. Compelling evidence support a core HIV-1 transcriptional program regulated by the sequential and concerted action of cellular transcription factors and Tat to promote initiation and sustain elongation, highlighting the efficiency of a small virus to take over its host to produce the high levels of transcription required for viral replication. I summarize new advances including the use of CRISPR-Cas9, genetic tools for acute factor depletion, and imaging to study transcriptional dynamics, bursting and the progression through the multiple phases of the transcriptional cycle. Finally, I describe current challenges to future major advances and discuss areas that deserve more attention to both bolster our basic knowledge of the core HIV-1 transcriptional program and open up new therapeutic opportunities.

Chromatin-based memory as a self-stabilizing influence on cell identity

Cell types are traditionally thought to be specified and stabilized by gene regulatory networks. Here, we explore how chromatin memory contributes to the specification and stabilization of cell states. Through pervasive, local, feedback loops, chromatin memory enables cell states that were initially unstable to become stable. Deeper appreciation of this self-stabilizing role for chromatin broadens our perspective of Waddington's epigenetic landscape from a static surface with islands of stability shaped by evolution, to a plasticine surface molded by experience. With implications for the evolution of cell types, stabilization of resistant states in cancer, and the widespread plasticity of complex life.

Low expression of CCKBR in the acinar cells is associated with insufficient starch hydrolysis in ruminants

Unlike monogastric animals, ruminants exhibit significantly lower starch digestibility in the small intestine. A better understanding of the physiological mechanisms that regulate digestion patterns in ruminants could lead to an increased use of starch concentrates. Here we show more robust pancreatic exocrine function in adult goats (AG) than in neonatal goats (NG) by combining scRNA-seq and proteomic analysis. Our findings suggest that inadequate amylase activity could be a limiting factor in starch digestion in ruminants. In addition, we show that insufficient starch hydrolysis in adult goats might be associated with low expression of a CCKBR receptor in the acinar cells. On top of that, the low expression of CCKBR in adult goats, in conjunction with a low distribution of the CCK-I cells in the duodenum, may jointly lead to a slow response of the intestinal-pancreatic reflex and induce an asynchronous process of food entering the small intestine and releasing of digestive enzymes, which ultimately limits the starch digestibility. Overall, the present findings generate a resource that can provide better insight into the mammalian pancreas.

Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise remains unclear. Here, we utilize a small-molecule perturbation (5'-iodo-2'-deoxyuridine [IdU]) to amplify noise and assess noise quantification from numerous single-cell RNA sequencing (scRNA-seq) algorithms on human and mouse datasets and then compare it to noise quantification from single-molecule RNA fluorescence in situ hybridization (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise-without altered mean expression levels-for ∼90% of genes and that smFISH analysis verifies noise amplification for the vast majority of tested genes. Collectively, the analyses suggest that most scRNA-seq algorithms (including a simple normalization approach) are appropriate for quantifying noise, although all algorithms appear to systematically underestimate noise changes compared to smFISH. For practical purposes, this analysis further argues that IdU noise enhancement is globally penetrant-i.e., homeostatically increasing noise without altering mean expression levels-and could enable investigations of the physiological impacts of transcriptional noise.

A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation

HSCs differentiation has been difficult to study experimentally due to the high number of components and interactions involved, as well as the impact of diverse physiological conditions. From a 200-node network, that was grounded on experimental data, we derived a 21-node regulatory network by collapsing linear pathways and retaining the functional feedback loops. This regulatory network core integrates key nodes and interactions underlying HSCs differentiation, including transcription factors, metabolic, and redox signaling pathways. We used Boolean, continuous, and stochastic dynamic models to simulate the hypoxic conditions of the HSCs niche, as well as the patterns and temporal sequences of HSCs transitions and differentiation. Our findings indicate that HSCs differentiation is a plastic process in which cell fates can transdifferentiate among themselves. Additionally, we found that cell heterogeneity is fundamental for HSCs differentiation. Lastly, we found that oxygen activates ROS production, inhibiting quiescence and promoting growth and differentiation pathways of HSCs.