Oliver Hilsenbeck

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.

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

BackgroundIn 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.ResultsIn 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.ConclusionsOur 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.

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.

Challenges in long-term imaging and quantification of single-cell dynamics

Continuous analysis of single cells, over several cell divisions and for up to weeks at a time, is crucial to deciphering rare, dynamic and heterogeneous cell responses, which would otherwise be missed by population or single-cell snapshot analysis. Although the field of long-term single-cell imaging, tracking and analysis is constantly advancing, several technical challenges continue to hinder wider implementation of this important approach. This is a particular problem for mammalian cells, where in vitro observation usually remains the only possible option for uninterrupted long-term, single-cell observation. Efforts must focus not only on identifying and maintaining culture conditions that support normal cellular behavior while allowing high-resolution imaging over time, but also on developing computational methods that enable semiautomatic analysis of the data. Solutions in microscopy hard- and software, computer vision and specialized theoretical methods for analysis of dynamic single-cell data will enable important discoveries in biology and beyond.

Software tools for single-cell tracking and quantification of cellular and molecular properties

Software tools for single-cell tracking and quantification of cellular and molecular properties

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.

Identification of factors promoting ex vivo maintenance of mouse hematopoietic stem cells by long-term single-cell quantification.

The maintenance of hematopoietic stem cells (HSCs) during ex vivo culture is an important prerequisite for their therapeutic manipulation. However, despite intense research, culture conditions for robust maintenance of HSCs are still missing. Cultured HSCs are quickly lost, preventing their improved analysis and manipulation. Identification of novel factors supporting HSC ex vivo maintenance is therefore necessary. Coculture with the AFT024 stroma cell line is capable of maintaining HSCs ex vivo long-term, but the responsible molecular players remain unknown. Here, we use continuous long-term single-cell observation to identify the HSC behavioral signature under supportive or nonsupportive stroma cocultures. We report early HSC survival as a major characteristic of HSC-maintaining conditions. Behavioral screening after manipulation of candidate molecules revealed that the extracellular matrix protein dermatopontin (Dpt) is involved in HSC maintenance. DPT knockdown in supportive stroma impaired HSC survival, whereas ectopic expression of the Dpt gene or protein in nonsupportive conditions restored HSC survival. Supplementing defined stroma- and serum-free culture conditions with recombinant DPT protein improved HSC clonogenicity. These findings illustrate a previously uncharacterized role of Dpt in maintaining HSCs ex vivo.

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.

CSF-1–induced Src signaling can instruct monocytic lineage choice

Controlled regulation of lineage decisions is imperative for hematopoiesis. Yet, the molecular mechanisms underlying hematopoietic lineage choices are poorly defined. Colony-stimulating factor 1 (CSF-1), the cytokine acting as the principal regulator of monocyte/macrophage (M) development, has been shown to be able to instruct the lineage choice of uncommitted granulocyte M (GM) progenitors toward an M fate. However, the intracellular signaling pathways involved are unknown. CSF-1 activates a multitude of signaling pathways resulting in a pleiotropic cellular response. The precise role of individual pathways within this complex and redundant signaling network is dependent on cellular context, and is not well understood. Here, we address which CSF-1-activated pathways are involved in transmitting the lineage-instructive signal in primary bone marrow-derived GM progenitors. Although its loss is compensated for by alternative signaling activation mechanisms, Src family kinase (SFK) signaling is sufficient to transmit the CSF-1 lineage instructive signal. Moreover, c-Src activity is sufficient to drive M fate, even in nonmyeloid cells.

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

Summary 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.