A multimodal zebrafish developmental atlas reveals the state-transition dynamics of late-vertebrate pluripotent axial progenitors.

Elucidating organismal developmental processes requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by reconstructions of cellular trajectories. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate-mapping experiments. To demonstrate the versatility of our multimodal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of neuro-mesodermal progenitors (NMPs) and the origins of a joint kidney-hemangioblast progenitor population.

Clearing the slate: RNA turnover to enable cell state switching?

The distribution of mRNA in tissue is determined by the balance between transcription and decay. Understanding the control of RNA decay during development has been somewhat neglected compared with transcriptional control. Here, we explore the potential for mRNA decay to trigger rapid cell state transitions during development, comparing a bistable switch model of cell state conversion with experimental evidence from different developmental systems. We also consider another potential role for large-scale RNA decay that has emerged from studies of stress-induced cell state transitions, in which removal of mRNA unblocks the translation machinery to prioritise the synthesis of proteins that establish the new cell state.

Collective signalling drives rapid jumping between cell states.

Development can proceed in 'fits and starts', with rapid transitions between cell states involving concerted transcriptome-wide changes in gene expression. However, it is not clear how these transitions are regulated in complex cell populations, in which cells receive multiple inputs. We address this issue using Dictyostelium cells undergoing development in their physiological niche. A continuous single cell transcriptomics time series identifies a sharp 'jump' in global gene expression marking functionally different cell states. By simultaneously imaging the physiological dynamics of transcription and signalling, we show the jump coincides with the onset of collective oscillations of cAMP. Optogenetic control of cAMP pulses shows that different jump genes respond to distinct dynamic features of signalling. Late jump gene expression changes are almost completely dependent on cAMP, whereas transcript changes at the onset of the jump require additional input. The coupling of collective signalling with gene expression is a potentially powerful strategy to drive robust cell state transitions in heterogeneous signalling environments. Based on the context of the jump, we also conclude that sharp gene expression transitions may not be sufficient for commitment.

The thylakoid phosphatase TEF8 is involved in state transition and high light stress resistance in Chlamydomonas.

The sophisticated regulation of state transition is required to maintain optimal photosynthetic performance under fluctuating light condition, through balancing the absorbed light energy between photosystem II and photosystem I. This exquisite process incorporates phosphorylation and dephosphorylation of light-harvesting complexes and PSII core subunits, accomplished by thylakoid membrane-localized kinases and phosphatases that have not been fully identified. In this study, one Chlamydomonas high light response gene, THYLAKOID ENRICHED FRACTION 8 (TEF8), was characterized. The Chlamydomonas tef8 mutant showed high light sensitivity and defective state transition. The enzymatic activity assays showed that TEF8 is a bona fide phosphatase localized in thylakoid membranes. Biochemical assays, including BN-PAGE, pull-down, and phosphopeptide mass spectrometry, proved that TEF8 associates with photosystem II and is involved in the dephosphorylation of D2 and CP29 subunits during state 2 to state 1 transition. Taken together, our results identified TEF8 as a thylakoid phosphatase with multiple dephosphorylation targets on photosystem II, and provide new insight into the regulatory mechanism of state transition and high light resistance in Chlamydomonas.

Interplay among photoreceptors determines the strategy of coping with excess light in tomato.

This study investigates photoreceptor's role in the adaption of photosynthetic apparatus to high light (HL) intensity by examining the response of tomato wild type (WT) (Solanum lycopersicum L. cv. Moneymaker) and tomato mutants (phyA, phyB1, phyB2, cry1) plants to HL. Our results showed a photoreceptor-dependent effect of HL on the maximum quantum yield of photosystem II (Fv/Fm) with phyB1 exhibiting a decrease, while phyB2 exhibiting an increase in Fv/Fm. HL resulted in an increase in the efficient quantum yield of photosystem II (ΦPSII) and a decrease in the non-photochemical quantum yields (ΦNPQ and ΦN0) solely in phyA. Under HL, phyA showed a significant decrease in the energy-dependent quenching component of NPQ (qE), while phyB2 mutants showed an increase in the state transition (qT) component. Furthermore, ΔΔFv/Fm revealed that PHYB1 compensates for the deficit of PHYA in phyA mutants. PHYA signaling likely emerges as the dominant effector of PHYB1 and PHYB2 signaling within the HL-induced signaling network. In addition, PHYB1 compensates for the role of CRY1 in regulating Fv/Fm in cry1 mutants. Overall, the results of this research provide valuable insights into the unique role of each photoreceptor and their interplay in balancing photon energy and photoprotection under HL condition.

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion.

Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.

Acute stress promotes brain network integration and reduces state transition variability.

Despite the prevalence of stress, how brains reconfigure their multilevel, hierarchical functional organization in response to acute stress remains unclear. We examined changes in brain networks after social stress using whole-brain resting-state functional MRI (fMRI) by extending our recently published nested-spectral partition method, which quantified the functional balance between network segregation and integration. Acute stress was found to shift the brain into a more integrated and less segregated state, especially in frontal-temporal regions. Stress also stabilized brain states by reducing the variability of dynamic transition between segregated and integrated states. Transition frequency was associated with the change of cortisol, and transition variability was correlated with cognitive control. Our results show that brain networks tend to be more integrated and less variable after acute stress, possibly to enable efficient coping.

Prospective and retrospective values integrated in frontal cortex drive predictive choice.

To make a deliberate action in a volatile environment, the brain must frequently reassess the value of each action (action-value). Choice can be initially made from the experience of trial-and-errors, but once the dynamics of the environment is learned, the choice can be made from the knowledge of the environment. The action-values constructed from the experience (retrospective value) and the ones from the knowledge (prospective value) were identified in various regions of the brain. However, how and which neural circuit integrates these values and executes the chosen action remains unknown. Combining reinforcement learning and two-photon calcium imaging, we found that the preparatory activity of neurons in a part of the frontal cortex, the anterior-lateral motor (ALM) area, initially encodes retrospective value, but after extensive training, they jointly encode the retrospective and prospective value. Optogenetic inhibition of ALM preparatory activity specifically abolished the expert mice's predictive choice behavior and returned them to the novice-like state. Thus, the integrated action-value encoded in the preparatory activity of ALM plays an important role to bias the action toward the knowledge-dependent, predictive choice behavior.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.SIGNIFICANCE STATEMENT The brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

Phylogenetic Profiling Analysis of the Phycobilisome Revealed a Novel State-Transition Regulator Gene in Synechocystis sp. PCC 6803.

Phycobilisomes play a crucial role in the light-harvesting mechanisms of cyanobacteria, red algae and glaucophytes, but the molecular mechanism of their regulation is largely unknown. In the cyanobacterium, Synechocystis sp. PCC 6803, we identified slr0244 as a phycobilisome-related gene using phylogenetic profiling analysis, a method used to predict gene function based on comparative genomics. To investigate the physiological function of the slr0244 gene, we characterized slr0244 mutants spectroscopically. Disruption of the slr0244 gene impaired state transition, a process by which the distribution of light energy absorbed by the phycobilisomes between two photosystems is regulated in response to the changes in light conditions. The Slr0244 protein seems to act in the process of state transition, somewhere at or downstream of the sensing step of the redox state of the plastoquinone (PQ) pool. These findings, together with past reports describing the interaction of this gene product with thioredoxin and glutaredoxin, suggest that the slr0244 gene is a novel state-transition regulator that integrates the redox signal of PQ pools with that of the photosystem I-reducing side. The protein has two universal stress protein (USP) motifs in tandem. The second motif has two conserved cysteine residues found in USPs of other cyanobacteria and land plants. These redox-type USPs with conserved cysteines may function as redox regulators in various photosynthetic organisms. Our study also shows the efficacy of phylogenetic profiling analysis in predicting the function of cyanobacterial genes that have not been annotated so far.

Zfp281 orchestrates interconversion of pluripotent states by engaging Ehmt1 and Zic2.

Developmental cell fate specification is a unidirectional process that can be reverted in response to injury or experimental reprogramming. Whether differentiation and de-differentiation trajectories intersect mechanistically is unclear. Here, we performed comparative screening in lineage-related mouse naïve embryonic stem cells (ESCs) and primed epiblast stem cells (EpiSCs), and identified the constitutively expressed zinc finger transcription factor (TF) Zfp281 as a bidirectional regulator of cell state interconversion. We showed that subtle chromatin binding changes in differentiated cells translate into activation of the histone H3 lysine 9 (H3K9) methyltransferase Ehmt1 and stabilization of the zinc finger TF Zic2 at enhancers and promoters. Genetic gain-of-function and loss-of-function experiments confirmed a critical role of Ehmt1 and Zic2 downstream of Zfp281 both in driving exit from the ESC state and in restricting reprogramming of EpiSCs. Our study reveals that cell type-invariant chromatin association of Zfp281 provides an interaction platform for remodeling the cis-regulatory network underlying cellular plasticity.

The alterations of repetitive transcranial magnetic stimulation on the energy landscape of resting-state networks differ across the human cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention tool for the noninvasive modulation of brain activity and behavior in neuroscience research and clinical settings. However, the resting-state dynamic evolution of large-scale functional brain networks following rTMS has rarely been investigated. Here, using resting-state fMRI images collected from 23 healthy individuals before (baseline) and after 1 Hz rTMS of the left frontal (FRO) and occipital (OCC) lobes, we examined the different effects of rTMS on brain dynamics across the human cortex. By fitting a pairwise maximum entropy model (pMEM), we constructed an energy landscape for the baseline and poststimulus conditions by fitting a pMEM. We defined dominant brain states (local minima) in the energy landscape with synergistic activation and deactivation patterns of large-scale functional networks. We calculated state dynamics including appearance probability, transitions and duration. The results showed that 1 Hz rTMS induced increased and decreased state probability, transitions and duration when delivered to the FRO and OCC targets, respectively. Most importantly, the shortest path and minimum cost between dominant brain states were altered after stimulation. The absolute sum of the costs from the source states to the destinations was lower after OCC stimulation than after FRO stimulation. In conclusion, our study characterized the dynamic trajectory of state transitions in the energy landscape and suggested that local rTMS can induce significant dynamic perturbation involving stimulated and distant functional networks, which aligns with the modern view of the dynamic and complex brain. Our results suggest low-dimensional mapping of rTMS-induced brain adaption, which will contribute to a broader and more effective application of rTMS in clinical settings.

Tailoring Ni-Fe-B Electronic Effects in Layered Double Hydroxides for Enhanced Oxygen Evolution Activity.

NiFe layered double hydroxides (LDHs) are state-of-the-art catalysts for the oxygen evolution reaction (OER) in alkaline media, yet they still face significant overpotentials. Here, quantitative boron (B) doping is introduced in NiFe LDHs (ranging from 0% to 20.3%) to effectively tailor the Ni-Fe-B electronic interactions for enhanced OER performance. The co-hydrolysis synthesis approach synchronizes the hydrolysis rates of Ni and Fe precursors with the formation rate of B─O─M (M: Ni, Fe) bonds, ensuring precise B doping into the NiFe LDHs. It is demonstrated that B, as an electron-deficient element, acts as an "electron sink" at doping levels from 0% to 13.5%, facilitating the transition of Ni2+ to the active Ni3+δ, thereby accelerating OER kinetics. However, excessive B doping (13.5-20.3%) effectively generates oxygen vacancies in the LDHs, which increases electron density at Ni2+ sites and hinders their transition to Ni3+δ, thereby reducing OER activity. Optimal OER performance is achieved at a B doping level of 13.5%, with an overpotential of only 208 mV to reach a current density of 500 mA cm-2, placing it among the most effective OER catalysts to date. This Ni-Fe-B electronic engineering opens new avenues for developing highly efficient anode catalysts for water-splitting hydrogen production.

Transition from totipotency to pluripotency in mice: insights into molecular mechanisms.

Totipotency is the ability of a single cell to develop into a full organism and, in mammals, is strictly associated with the early stages of development following fertilization. This unlimited developmental potential becomes quickly restricted as embryonic cells transition into a pluripotent state. The loss of totipotency seems a consequence of the zygotic genome activation (ZGA), a process that determines the switch from maternal to embryonic transcription, which in mice takes place following the first cleavage. ZGA confers to the totipotent cell a transient transcriptional profile characterized by the expression of stage-specific genes and a set of transposable elements that prepares the embryo for subsequent development. The timely silencing of this transcriptional program during the exit from totipotency is required to ensure proper development. Importantly, the molecular mechanisms regulating the transition from totipotency to pluripotency have remained elusive due to the scarcity of embryonic material. However, the development of new in vitro totipotent-like models together with advances in low-input genome-wide technologies, are providing a better mechanistic understanding of how this important transition is achieved. This review summarizes the current knowledge on the molecular determinants that regulate the exit from totipotency.

The blue-green light-dependent state transition in the marine phytoplankton Ostreococcus tauri.

We explored the adaptive mechanisms of Ostreococcus tauri, a marine picophytoplankton with a ubiquitous ocean presence. We aimed to understand its photosynthetic acclimation, as featured in the cryo-EM structure of its photosystem I (PSI) supercomplex. This structure revealed a unique composition involving a phosphorylated Lhcp trimer bound to the PSI core along with two additional Lhcp trimers, suggesting potential state transitions for photoacclimation. To investigate this hypothesis, we conducted a series of biochemical and physiological experiments. We analyzed absorption spectra to differentiate between PSI and PSII, particularly focusing on blue-green wavelengths, and examined the effects of specific excitation of Lhcp with green light, including its phosphorylation and the formation of the PSI-LHCI-Lhcp supercomplex. Our experiments clarified the distinctive effects attributable to absorption by pigments associated with Lhcp. Exciting Lhcp with green light induced its phosphorylation, leading to the formation of the PSI-LHCI-Lhcp supercomplex. Notably, the functional antenna size of PSI could reversibly expand in response to green light, demonstrating its state transition capability. These findings not only highlight the unique photosynthetic acclimation adapted to the marine environment but also suggest a possible ancestral role of state transitions in green plants, given the phylogenetic position of Prasinophyceae.

State transition and intercellular communication of synovial fibroblasts in response to chronic and acute shoulder injuries unveiled by single-cell transcriptomic analyses.

PURPOSE: We aimed to investigate the heterogeneity of synovial fibroblasts and their potential to undergo cell state transitions at the resolution of single cells. MATERIALS AND METHODS: We employed the single-cell RNA sequencing (scRNA-seq) approach to comprehensively map the cellular landscape of the shoulder synovium in individuals with chronic rotator cuff tears (RCTs) and acute proximal humerus fractures (PHFs). Utilizing unbiased clustering analysis, we successfully identified distinct subpopulations of fibroblasts within the synovial environment. We utilized Monocle 3 to delineate the trajectory of synovial fibroblast state transition. And we used CellPhone DB v2.1.0 to predict cell-cell communication patterns within the synovial microenvironment. RESULTS: We identified eight main cell clusters in the shoulder synovium. Unbiased clustering analysis identified four synovial fibroblast subpopulations, with diverse biological functions associated with protein secretion, ECM remodeling, inflammation regulation and cell division. Lining, mesenchymal, pro-inflammatory and proliferative fibroblasts subsets were identified. Combining the results from StemID and characteristic gene features, mesenchymal fibroblasts exhibited characteristics of fibroblast progenitor cells. The trajectory of synovial fibroblast state transition showed a transition from mesenchymal to pro-inflammatory and lining phenotypes. In addition, the cross talk between fibroblast subclusters increased in degenerative shoulder diseases compared to acute trauma. CONCLUSION: We successfully generated the scRNA-seq transcriptomic atlas of the shoulder synovium, which provides a comprehensive understanding of the heterogeneity of synovial fibroblasts, their potential to undergo state transitions, and their intercellular communication in the context of chronic degenerative and acute traumatic shoulder diseases.

State transitions across the Strep A disease spectrum: scoping review and evidence gaps.

The spectrum of diseases caused by Streptococcus pyogenes (Strep A) ranges from superficial to serious life-threatening invasive infections. We conducted a scoping review of published articles between 1980 and 2021 to synthesize evidence of state transitions across the Strep A disease spectrum. We identified 175 articles reporting 262 distinct observations of Strep A disease state transitions. Among the included articles, the transition from an invasive or toxin-mediated disease state to another disease state (i.e., to recurrent ARF, RHD or death) was described 115 times (43.9% of all included transition pairs) while the transition to and from locally invasive category was the lowest (n = 7; 0.02%). Transitions from well to any other state was most frequently reported (49%) whereas a relatively higher number of studies (n = 71) reported transition from invasive disease to death. Transitions from any disease state to locally invasive, Strep A pharyngitis to invasive disease, and chronic kidney disease to death were lacking. Transitions related to severe invasive diseases were more frequently reported than superficial ones. Most evidence originated from high-income countries and there is a critical need for new studies in low- and middle-income countries to infer the state transitions across the Strep A disease spectrum in these high-burden settings.

Ketamine and the paradox of anaesthetic state transitions.

Administration of subanaesthetic doses of ketamine during isoflurane anaesthesia has been shown in animals to deepen the anaesthetised state, while accelerating emergence. Duan and colleagues have now shown that the addition of subanaesthetic doses of esketamine to isoflurane has a similar effect of increasing the burst suppression ratio, while accelerating emergence. Using c-Fos expression and fibre photometry, they show that esketamine activates glutamatergic neurones in the paraventricular nucleus of the thalamus, a structure that regulates wakefulness. Chemogenetic inhibition of these neurones attenuates the arousal-promoting effects, suggesting a causal role of the paraventricular nucleus of the thalamus in esketamine-mediated acceleration of recovery from anaesthesia.

Rapid Surface Reconstruction of Pentlandite by High-Spin State Iron for Efficient Oxygen Evolution Reaction.

Triggering rapid reconstruction reactions holds the potential to approach the theoretical limits of the oxygen evolution reaction (OER), and spin state manipulation has shown great promise in this regard. In this study, the transition of Fe spin states from low to high was successfully achieved by adjusting the surface electronic structure of pentlandite. In situ characterization and kinetic simulations confirmed that the high-spin state of Fe promoted the accumulation of OH- on the surface and accelerated electron transfer, thereby enhancing the kinetics of the reconstruction reaction. Furthermore, theoretical calculations revealed that the lower d-band center of high-spin Fe optimized the adsorption of active intermediates, thereby enhancing the reconstruction kinetics. Remarkably, pentlandites with high-spin Fe exhibited ultra-low overpotential (245 mV @ 10 mA cm-2 ) and excellent stability. These findings provided new insights for the design and fabrication of highly active OER electrocatalysts.

Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study.

Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project. Simultaneously, we defined a novel similarity measure, termed the Multivariate Time-series Ensemble Similarity Score (MTESS). MTESS showed high accuracy and f-measure in subject identification, and it can directly compare the similarity between two multivariate time-series. We used MTESS to analyze both human and marmoset rs-fMRI data. Our results showed similarity differences between cortical and subcortical regions. We also conducted MTESS and state transition analysis between single and group surrogate techniques, and confirmed that a group surrogate approach can generate plausible group centroid multivariate time-series. Finally, we used GSDGM and MTESS for the fingerprint analysis of human rs-fMRI data, successfully distinguishing normal and outlier sessions. These new techniques will be useful for clinical applications and in silico simulation.