Biology subjects

Berg, J.

Publications and source records attributed to Berg, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A sulfur-cycling co-culture alters iron sulfide diagenesis and pyritization

Iron sulfide minerals play a key role in modulating the oxygenation of Earth s atmosphere and represent an archive for reconstructing past environmental conditions. In anoxic sedimentary environments, microbially produced sulfide reacts with iron, thereby forming metastable iron sulfides (mackinawite, greigite) which are expected to transform into pyrite, the most thermodynamically stable phase. However, our understanding of this process mostly stems from pure cultures grown on sulfate, which fails to capture the complexity of natural microbial communities and subsequent sedimentary changes. To this end, we set up pure and co-culture incubations of sulfate- and sulfur-reducing bacteria with selected ferric minerals (goethite, ferric phosphate and ferrihydrite) or ferrous iron and later subjected the samples to experimental diagenesis at controlled temperature (80 degrees C). We hypothesized that the co-culture would enhance pyrite formation by maintaining an internal cryptic S cycle that replenishes polysulfides and ferrous iron via the sulfidation of ferric compounds. Our findings showed that iron sulfide morphology was strongly dependent on the reactivity of ferric compounds, rather than the presence of bacteria. Consistent with this, pyrite formed through diagenesis in both pure cultures and abiotic incubations amended with sodium sulfide, indicating that pyritization is a primarily chemically controlled process. Contrary to our expectations, the presence of the co-culture led to longer diagenetic preservation of metastable minerals like greigite and marcasite, ultimately hindering pyritization. Surprisingly, we could also detect spherulitic pyrite after experimental diagenesis in sterile samples without exogenous sulfide addition. This was explained by the abiotic disproportionation of elemental S which took place in temperature regimes usually associated with microbial sulfide production. Overall, these results highlight the influence of mixed microbial communities, but also overlooked abiotic reactions, on iron sulfide (trans)formations in sedimentary environments, with implications for paleo(magnetic) reconstructions and the usability of these minerals as biosignatures.

microbiology↗

bioRxiv2026

Depletion of Chloroplast HSP70B Triggers Proteostasis Collapse and Compromises Thylakoid Membrane Integrity in Chlamydomonas

Chloroplast HSP70 is an essential component of the plastid proteostasis network, supporting protein folding, complex assembly and disassembly, and stress acclimation. Despite extensive genetic evidence for its essentiality, the cellular consequences of reduced chloroplast HSP70 activity remain poorly defined. Here, we investigated the function of the sole chloroplast HSP70 in Chlamydomonas reinhardtii, HSP70B, using an inducible artificial microRNA approach that reduced HSP70B abundance to below 30% of wild-type levels. HSP70B depletion resulted in cell division arrest and extensive proteome remodeling, characterized by strong upregulation of proteins involved in chloroplast protein quality control and membrane remodeling. Notably, this response was accompanied by increased abundance of protein quality control components in the endoplasmic reticulum, cytosol, and mitochondria, indicating pronounced proteostasis cross-talk between cellular compartments. In contrast, chloroplast and cytosolic ribosomes, photosynthetic and respiratory protein complexes, and central metabolic enzymes were broadly depleted, consistent with a collapse of cellular proteostasis. At the ultrastructural level, HSP70B-depleted cells exhibited lesions at thylakoid membrane conversion zones previously described in VIPP1-depleted cells. Accordingly, higher-order oligomeric forms of VIPP1 accumulated, and cells displayed extreme sensitivity to high-light stress. These findings confirm HSP70B as a key regulator of VIPP1 oligomer dynamics and highlight its central role in coordinating chloroplast membrane remodeling with cellular proteostasis in Chlamydomonas. One-sentence summaryDepletion of chloroplast HSP70B causes cell division arrest, proteostasis collapse, impaired VIPP1 oligomer dynamics with aberrant thylakoid structures, and increased light sensitivity.

plant biology↗

bioRxiv2025

An integrated model of population growth saturation and basal ROS levels predicts cellular ferroptosis sensitivity

Ferroptosis is a form of regulated cell death characterized by iron-dependent lipid peroxidation and membrane rupture. While cellular populations reaching confluence are known to have limited sensitivity to ferroptosis, an understanding of the interplay between growth dynamics, ROS levels and ferroptosis is currently lacking. Here we use live-cell imaging coupled to ROS tracing to reveal a feedback loop between population growth and ferroptotic cell death. Starting out from the observation that the cellular proliferation rate declines with increased cellular density, we find that ROS levels also decline with increasing cellular density. Low ROS levels make cells insensitive to ferroptosis, which in turn enables population growth. This feedback produces two steady states: (i) a ferroptosis-insensitive state characterized by slow growth, low levels of ROS and low rates of cell death and (ii) a ferroptosis-sensitive state characterized by rapid growth, ROS accumulation, and high rates of ferroptosis. Interestingly, triggering effective ferroptosis by interfering with GPX4 activity is directly linked with this mechanism. On the other hand, keeping cell numbers and drug concentration/cell constant while restricting growth space led to reduced proliferation, reduced ROS and decreased ferroptotic cell death. Importantly, ferroptosis resistance at high cellular confluency could be broken by increasing cellular ROS and lipid ROS through a galactose-promoted OXPHOS switch. A mathematical model of the feedback mechanism predicts the long-term fate of populations as well as their ferroptosis sensitivity when external conditions impacting cell proliferation rates, ROS, or both are changed.

cancer biology↗

bioRxiv2025

Esophageal adenocarcinoma relapse after chemoradiation is dominated by a basal-like subtype

Neoadjuvant chemoradiation therapy (RCT) is a frequently used treatment regimen for esophageal adenocarcinoma (EAC); however, the response varies dramatically, and resistance is a clinical challenge. We aimed to identify the molecular mechanisms underlying RCT resistance. We established a mouse xenograft RCT model with human EAC cell lines representing different response groups, and tested enhanced genomic instability as a potential evolutionary modulator by reducing BRCA2 function. Xenografts that relapsed after RCT displayed upregulation of stress response keratins, including KRT6 and KRT16 connected with a basal-like transcriptomic/ proteomic phenotype. We screened our cohort of 728 patients with EAC and found significantly shorter overall survival for patients with KRT6-high tumors, driven by patients receiving neoadjuvant treatment. Overall, we identified a basal-like cell state in EAC that reflects RCT relapse. The basal-like subtype is a marker of treatment failure, providing a new avenue for translational research to overcome RCT resistance.

cancer biology↗

bioRxiv2024

Phylodynamic inference on single-cell data

Understanding the evolutionary dynamics of cell populations requires models that link observed phylogenetic patterns to the underlying processes of cell division, death, and mutation. Classical phylodynamic inference methods---developed primarily for macroevolutionary settings--assume that mutations accrue in calendar time and often rely on a molecular clock. Here, we introduce a framework that ties mutations to discrete birth (division) events. In this setting, mutations accumulate via a compound Poisson process, capturing both visible and hidden cell divisions within the reconstructed phylogenetic tree. We present a computationally efficient dynamic programming algorithm to compute the likelihood based on tree topologies with associated mutations, integrating over latent variables such as branch durations and unobserved cell divisions. Our method is applicable to large-scale single-cell datasets, and we demonstrate its utility on simulated data and on single-cell phylogenies of haematopoietic stem cells.

evolutionary biology↗

bioRxiv2023

High-density sampling reveals volume growth in human tumours

In growing cell populations such tumours, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analyses of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology.

cancer biology↗

bioRxiv2023

The microenvironment dictates glycocalyx construction and immune surveillance

Efforts to identify anti-cancer therapeutics and understand tumor-immune interactions are built with in vitro models that do not match the microenvironmental characteristics of human tissues. Using in vitro models which mimic the physical properties of healthy or cancerous tissues and a physiologically relevant culture medium, we demonstrate that the chemical and physical properties of the microenvironment regulate the composition and topology of the glycocalyx. Remarkably, we find that cancer and age-related changes in the physical properties of the microenvironment are sufficient to adjust immune surveillance via the topology of the glycocalyx, a previously unknown phenomenon observable only with a physiologically relevant culture medium. Key PointsO_LICulture medium dictates cellular mechanoresponse signatures in vitro C_LIO_LIEpithelial glycocalyx construction is mediated by Heat Shock Factor 1 (HSF1) C_LIO_LISialic acid topology dictates Natural Killer cell cytotoxicity C_LIO_LIPhysiological microenvironments reveal distinct glycobiology C_LI

cell biology↗

bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

neuroscience↗

bioRxiv2022

Morpho-electric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex

Neocortical layer 1 (L1) is a site of convergence between pyramidal neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neuron types with extensive branching in L1, but whether L1 interneurons are similarly diverse is underexplored. Using patch-seq recordings from human neurosurgically resected tissues, we identified four transcriptomically defined subclasses, unique subtypes within those subclasses and additional types with no mouse L1 homologue. Compared with mouse, human subclasses were more strongly distinct from each other across all modalities. Accompanied by higher neuron density and more variable cell sizes compared with mouse, these findings suggest L1 is an evolutionary hotspot, reflecting the increasing demands of regulating the expanding human neocortical circuit. One Sentence SummaryUsing transcriptomics and morpho-electric analyses, we describe innovations in human neocortical layer 1 interneurons.

neuroscience↗

bioRxiv2020

Modeling Research Project Grant Success Rates from NIH Appropriation History: Extension to 2020

Communication of the likelihood that a grant proposal is funded is important for individual researchers and academic institutions. For research project grants (RPGs) funded by the US National Institutes of Health (NIH), this is measured by the success rate, the fraction of grants reviewed in a given fiscal year that are funded. In 2016, I posted a relatively simple computational model that allowed estimation of these success rates from historical data of NIH appropriations and associated data, based largely on the fact that commitments to fund NIH grants are typically made over four fiscal years rather than coming from a single fiscal years appropriations. Data for appropriations and success rates for fiscal years 2016-2020 are now available, allowing this model to be tested without no adjustable parameters. Over this period, the NIH appropriation increased by 29.7% to $39.313 billion, yet the RPG success rate increased from 0.183 to 0.201, a relative increase of only 9.8%. This difference is accurately reproduced by the model, indicating that the modest changes in success rate despite large appropriation increases reflect the consequences of the funding grants from multiple fiscal years and the response of the biomedical research community in submitting an increased number of proposals. The model can also be extended to estimate the success rates for individual institutes with reasonable accuracy.

scientific communication and education↗

bioRxiv2020

High fidelity electrophysiological, morphological, and transcriptomic cell characterization using a refined Patch-seq protocol

The Patch-seq approach is a powerful variation of the standard patch clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at a scale and quality that can be integrated with high-throughput dissociated cell transcriptomic data, we have optimized the technique by identifying and refining key factors that contribute to the efficient collection of high-quality data. To rapidly generate high-quality electrophysiology data, we developed patch clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized a substantial improvement in transcriptomic data quality when the nucleus was extracted following the recording. For morphology success, the importance of maximizing the neurons membrane integrity during the extraction of the nucleus was much more critical to success than varying the duration of the electrophysiology recording. We compiled the lab protocol with the analysis and acquisition software at https://github.com/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate Patch-seq data across diverse mammalian species and that is compatible with recent large-scale publicly available Allen Institute Patch-seq datasets.

neuroscience↗

bioRxiv2020

A multimodal cell census and atlas of the mammalian primary motor cortex

We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

neuroscience↗

bioRxiv2020

Multi-modal characterization and simulation of human epileptic circuitry

Temporal lobe epilepsy is the fourth most common neurological disorder with about 40% of patients not responding to pharmacological treatment. Increased cellular loss in the hippocampus is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions such as temporal lobe resection, the impact of the disease at the cellular level remains unclear in humans. Here we show that properties of hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from epilepsy patients. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume, surface area and spine density. Single-cell RNA sequencing combined with simulations ascribe the observed electrophysiological changes to gradual modification in three key ion channel conductances: BK, Cav2.2 and Kir2.1. In a bio-realistic computational network model, we show that the changes related to disease progression bring the circuit into a more excitable state. In turn, we observe that by reversing these changes in the three key conductances produces a less excitable, "early disease-like" state. These results provide mechanistic understanding of epilepsy in humans and will inform future therapies such as viral gene delivery to reverse the course of the disorder.

neuroscience↗

bioRxiv2020

Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined. HighlightsO_LIGeneration and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments. C_LIO_LIOptimized ion channel conductances along the cellular morphology ( all-active) are characteristic of model complexity and offer enhanced biophysical realism. C_LIO_LIIon channel conductance vectors of all-active models classify transcriptomically defined cell-types. C_LIO_LICell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes. C_LIO_LIA set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments. C_LIO_LIAll-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them. C_LI

neuroscience↗

bioRxiv2020

Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons

The neocortex is disproportionately expanded in human compared to mouse, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers that selectively make connections within the cortex and other telencephalic structures. Single-cell transcriptomic analyses of human and mouse cortex show an increased diversity of glutamatergic neuron types in supragranular cortex in human and pronounced gradients as a function of cortical depth. To probe the functional and anatomical correlates of this transcriptomic diversity, we describe a robust Patch-seq platform using neurosurgically-resected human tissues. We characterize the morphological and physiological properties of five transcriptomically defined human glutamatergic supragranular neuron types. Three of these types have properties that are specialized compared to the more homogeneous properties of transcriptomically defined homologous mouse neuron types. The two remaining supragranular neuron types, located exclusively in deep layer 3, do not have clear mouse homologues in supragranular cortex but are transcriptionally most similar to deep layer mouse intratelencephalic-projecting neuron types. Furthermore, we reveal the transcriptomic types in deep layer 3 that express high levels of non-phosphorylated heavy chain neurofilament protein that label long-range neurons known to be selectively depleted in Alzheimers disease. Together, these results demonstrate the power of transcriptomic cell type classification, provide a mechanistic underpinning for increased complexity of cortical function in human cortical evolution, and implicate discrete transcriptomic cell types as selectively vulnerable in disease.

neuroscience↗

bioRxiv2019

In vitro model for resistance in oncogene-dependent tumors at the limit of radiological detectability

In solid tumors, the response to targeted therapy is typically short-lived, as therapy-resistant mutants can quickly expand during therapy. Here we analyze the spectrum of such resistance mutations coexisting in a large population of cancer cells. We use an iterative scheme of artificial evolution to amplify and isolate different resistance mechanisms. As a proof of concept, we apply our scheme to PC-9 cells, a human non-small cell lung cancer cell line with an activating EGFR mutation. The mechanisms we find comprise the well-known gatekeeper-mutation T790M in EGFR, a mutation in NRAS, the amplification of MET-ligand HGF, as well as induction of AKT-mTOR signaling. In this model, a combination of four drugs targeting these mechanisms prevents not only the expansion of resistant cells, but also inhibits the growth of drug-tolerant cells, which can otherwise act as a reservoir for further resistance mutations. These data suggest that a finite number of drugs specifically acting on individual resistant clones may be able to control resistance in oncogenically driven lung cancer.

cancer biology↗

bioRxiv2018

Classification of electrophysiological and morphological types in mouse visual cortex

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.

neuroscience↗

bioRxiv2018

Visual physiology of the Layer 4 cortical circuit in silico

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.\n\nAUTHOR SUMMARYHow can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 - in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.

neuroscience↗