Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios.

The mechanisms underlying haematopoietic lineage decisions remain disputed. Lineage-affiliated transcription factors with the capacity for lineage reprogramming, positive auto-regulation and mutual inhibition have been described as being expressed in uncommitted cell populations. This led to the assumption that lineage choice is cell-intrinsically initiated and determined by stochastic switches of randomly fluctuating cross-antagonistic transcription factors. However, this hypothesis was developed on the basis of RNA expression data from snapshot and/or population-averaged analyses. Alternative models of lineage choice therefore cannot be excluded. Here we use novel reporter mouse lines and live imaging for continuous single-cell long-term quantification of the transcription factors GATA1 and PU.1 (also known as SPI1). We analyse individual haematopoietic stem cells throughout differentiation into megakaryocytic-erythroid and granulocytic-monocytic lineages. The observed expression dynamics are incompatible with the assumption that stochastic switching between PU.1 and GATA1 precedes and initiates megakaryocytic-erythroid versus granulocytic-monocytic lineage decision-making. Rather, our findings suggest that these transcription factors are only executing and reinforcing lineage choice once made. These results challenge the current prevailing model of early myeloid lineage choice.

Prospective identification of hematopoietic lineage choice by deep learning.

Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage choice can be detected up to three generations before conventional molecular markers are observable. Our approach allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.

fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy.

MOTIVATION: Quantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.g. high cell densities, cell-to-cell variability and low signal-to-noise ratios. RESULTS: Here, we evaluate accuracy and speed of various state-of-the-art approaches for cell segmentation in light microscopy images using challenging real and synthetic image data. The results vary between datasets and show that the tested tools are either not robust enough or computationally expensive, thus limiting their application to large-scale experiments. We therefore developed fastER, a trainable tool that is orders of magnitude faster while producing state-of-the-art segmentation quality. It supports various cell types and image acquisition modalities, but is easy-to-use even for non-experts: it has no parameters and can be adapted to specific image sets by interactively labelling cells for training. As a proof of concept, we segment and count cells in over 200 000 brightfield images (1388 × 1040 pixels each) from a six day time-lapse microscopy experiment; identification of over 46 000 000 single cells requires only about two and a half hours on a desktop computer. AVAILABILITY AND IMPLEMENTATION: C ++ code, binaries and data at https://www.bsse.ethz.ch/csd/software/faster.html . CONTACT: oliver.hilsenbeck@bsse.ethz.ch or timm.schroeder@bsse.ethz.ch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evidence of autoinducer-dependent and -independent heterogeneous gene expression in Sinorhizobium fredii NGR234.

Populations of genetically identical Sinorhizobium fredii NGR234 cells differ significantly in their expression profiles of autoinducer (AI)-dependent and AI-independent genes. Promoter fusions of the NGR234 AI synthase genes traI and ngrI showed high levels of phenotypic heterogeneity during growth in TY medium on a single-cell level. However, adding very high concentrations of N-(3-oxooctanoyl-)-l-homoserine lactone resulted in a more homogeneous expression profile. Similarly, the lack of internally synthesized AIs in the background of the NGR234-ΔtraI or the NGR234-ΔngrI mutant resulted in a highly homogenous expression of the corresponding promoter fusions in the population. Expression studies with reporter fusions of the promoter regions of the quorum-quenching genes dlhR and qsdR1 and the type IV pilus gene cluster located on pNGR234b suggested that factors other than AI molecules affect NGR234 phenotypic heterogeneity. Further studies with root exudates and developing Arabidopsis thaliana seedlings provide the first evidence that plant root exudates have strong effects on the heterogeneity of AI synthase and quorum-quenching genes in NGR234. Therefore, plant-released octopine appears to play a key role in modulation of heterogeneous gene expression.

An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy.

BACKGROUND: In recent years, high-throughput microscopy has emerged as a powerful tool to analyze cellular dynamics in an unprecedentedly high resolved manner. The amount of data that is generated, for example in long-term time-lapse microscopy experiments, requires automated methods for processing and analysis. Available software frameworks are well suited for high-throughput processing of fluorescence images, but they often do not perform well on bright field image data that varies considerably between laboratories, setups, and even single experiments. RESULTS: In this contribution, we present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. The pipeline comprises two steps: (i) Image acquisition is adjusted to obtain optimal bright field image quality for automatic processing. (ii) A concatenation of fast performing image processing algorithms robustly identifies single cells in each image. We applied the method to a time-lapse movie consisting of ∼315,000 images of differentiating hematopoietic stem cells over 6 days. We evaluated the accuracy of our method by comparing the number of identified cells with manual counts. Our method is able to segment images with varying cell density and different cell types without parameter adjustment and clearly outperforms a standard approach. By computing population doubling times, we were able to identify three growth phases in the stem cell population throughout the whole movie, and validated our result with cell cycle times from single cell tracking. CONCLUSIONS: Our method allows fully automated processing and analysis of high-throughput bright field microscopy data. The robustness of cell detection and fast computation time will support the analysis of high-content screening experiments, on-line analysis of time-lapse experiments as well as development of methods to automatically track single-cell genealogies.

The Sox17-mCherry fusion mouse line allows visualization of endoderm and vascular endothelial development.

Sox17 is a HMG-box transcription factor that has been shown to play important roles in both cardio-vascular development and endoderm formation. To analyze these processes in greater detail, we have generated a Sox17-mCherry fusion (SCF) protein by gene targeting in ES cells. SCF reporter mice are homozygous viable and faithfully reflect the endogenous Sox17 protein localization. We report that SCF positive cells constitute a subpopulation in the visceral endoderm before gastrulation and time-lapse imaging reveals that SCF monitors the nascent definitive endoderm during epithelialization. After gastrulation, SCF marks the mid- and hindgut endoderm and vascular endothelial network, which can be imaged during establishment in allantois explant cultures. The SCF reporter is downregulated in the endoderm epithelium and upregulated in endothelial cells of the intestine, lung, and pancreas during organogenesis. In summary, the generation of the Sox17-mCherry reporter mouse line allows direct visualization of endoderm and vascular development in culture and the mouse embryo.

Inductive and Selective Effects of GSK3 and MEK Inhibition on Nanog Heterogeneity in Embryonic Stem Cells.

Embryonic stem cells (ESCs) display heterogeneous expression of pluripotency factors such as Nanog when cultured with serum and leukemia inhibitory factor (LIF). In contrast, dual inhibition of the signaling kinases GSK3 and MEK (2i) converts ESC cultures into a state with more uniform and high Nanog expression. However, it is so far unclear whether 2i acts through an inductive or selective mechanism. Here, we use continuous time-lapse imaging to quantify the dynamics of death, proliferation, and Nanog expression in mouse ESCs after 2i addition. We show that 2i has a dual effect: it both leads to increased cell death of Nanog low ESCs (selective effect) and induces and maintains high Nanog levels (inductive effect) in single ESCs. Genetic manipulation further showed that presence of NANOG protein is important for cell viability in 2i medium. This demonstrates complex Nanog-dependent effects of 2i treatment on ESC cultures.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells.

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.

Network plasticity of pluripotency transcription factors in embryonic stem cells.

Transcription factor (TF) networks are thought to regulate embryonic stem cell (ESC) pluripotency. However, TF expression dynamics and regulatory mechanisms are poorly understood. We use reporter mouse ESC lines allowing non-invasive quantification of Nanog or Oct4 protein levels and continuous long-term single-cell tracking and quantification over many generations to reveal diverse TF protein expression dynamics. For cells with low Nanog expression, we identified two distinct colony types: one re-expressed Nanog in a mosaic pattern, and the other did not re-express Nanog over many generations. Although both expressed pluripotency markers, they exhibited differences in their TF protein correlation networks and differentiation propensities. Sister cell analysis revealed that differences in Nanog levels are not necessarily accompanied by differences in the expression of other pluripotency factors. Thus, regulatory interactions of pluripotency TFs are less stringently implemented in individual self-renewing ESCs than assumed at present.

Multi-scale modeling of GMP differentiation based on single-cell genealogies.

Hematopoiesis is often pictured as a hierarchy of branching decisions, giving rise to all mature blood cell types from stepwise differentiation of a single cell, the hematopoietic stem cell. Various aspects of this process have been modeled using various experimental and theoretical techniques on different scales. Here we integrate the more common population-based approach with a single-cell resolved molecular differentiation model to study the possibility of inferring mechanistic knowledge of the differentiation process. We focus on a sub-module of hematopoiesis: differentiation of granulocyte-monocyte progenitors (GMPs) to granulocytes or monocytes. Within a branching process model, we infer the differentiation probability of GMPs from the experimentally quantified heterogeneity of colony assays under permissive conditions where both granulocytes and monocytes can emerge. We compare the predictions with the differentiation probability in genealogies determined from single-cell time-lapse microscopy. In contrast to the branching process model, we found that the differentiation probability as determined by differentiation marker onset increases with the generation of the cell within the genealogy. To study this feature from a molecular perspective, we established a stochastic toggle switch model, in which the intrinsic lineage decision is executed using two antagonistic transcription factors. We identified parameter regimes that allow for both time-dependent and time-independent differentiation probabilities. Finally, we infer parameters for which the model matches experimentally observed differentiation probabilities via approximate Bayesian computing. These parameters suggest different timescales in the dynamics of granulocyte and monocyte differentiation. Thus we provide a multi-scale picture of cell differentiation in murine GMPs, and illustrate the need for single-cell time-resolved observations of cellular decisions.