Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma.

Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

A spatially resolved timeline of the human maternal-fetal interface.

Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodelling (SAR)1-3. However, it remains a matter of debate regarding which immune and stromal cells participate in these interactions and how this evolves with respect to gestational age. Here we used a multiomics approach, combining the strengths of spatial proteomics and transcriptomics, to construct a spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time-of-flight and a 37-plex antibody panel to analyse around 500,000 cells and 588 arteries within intact decidua from 66 individuals between 6 and 20 weeks of gestation, integrating this dataset with co-registered transcriptomics profiles. Gestational age substantially influenced the frequency of maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 and IDO-1 becoming increasingly enriched and colocalized at later time points. By contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting distinct monotonic and biphasic trends. Last, we developed an integrated model of SAR whereby invasion is accompanied by the upregulation of pro-angiogenic, immunoregulatory EVT programmes that promote interactions with the vascular endothelium while avoiding the activation of maternal immune cells.

Global shape of Toll activation is determined by wntD enhancer properties.

Buffering variability in morphogen distribution is essential for reproducible patterning. A theoretically proposed class of mechanisms, termed "distal pinning," achieves robustness by combining local sensing of morphogen levels with global modulation of gradient spread. Here, we demonstrate a critical role for morphogen sensing by a gene enhancer, which ultimately determines the final global distribution of the morphogen and enables reproducible patterning. Specifically, we show that, while the pattern of Toll activation in the early Drosophila embryo is robust to gene dosage of its locally produced regulator, WntD, it is sensitive to a single-nucleotide change in the wntD enhancer. Thus, enhancer properties of locally produced WntD directly impinge on the global morphogen profile.

Dynamics of Spaetzle morphogen shuttling in the Drosophila embryo shapes gastrulation patterning.

Establishment of morphogen gradients in the early Drosophila embryo is challenged by a diffusible sextracellular milieu, and by rapid nuclear divisions that occur at the same time. To understand how a sharp gradient is formed within this dynamic environment, we followed the generation of graded nuclear Dorsal protein, the hallmark of pattern formation along the dorso-ventral axis, in live embryos. The dynamics indicate that a sharp extracellular gradient is formed through diffusion-based shuttling of the Spaetzle (Spz) morphogen that progresses through several nuclear divisions. Perturbed shuttling in wntD mutant embryos results in a flat activation peak and aberrant gastrulation. Re-entry of Dorsal into the nuclei at the final division cycle plays an instructive role, as the residence time of Dorsal in each nucleus is translated to the amount of zygotic transcript that will be produced, thereby guiding graded accumulation of specific zygotic transcripts that drive patterned gastrulation. We conclude that diffusion-based ligand shuttling, coupled with dynamic readout, establishes a refined pattern within the diffusible environment of early embryos.

Scaling morphogen gradients during tissue growth by a cell division rule.

Morphogen gradients guide the patterning of tissues and organs during the development of multicellular organisms. In many cases, morphogen signaling is also required for tissue growth. The consequences of this interplay between growth and patterning are not well understood. In the Drosophila wing imaginal disc, the morphogen Dpp guides patterning and is also required for tissue growth. In particular, it was recently reported that cell division in the disc correlates with the temporal increase in Dpp signaling. Here we mathematically model morphogen gradient formation in a growing tissue, accounting also for morphogen advection and dilution. Our analysis defines a new scaling mechanism, which we term the morphogen-dependent division rule (MDDR): when cell division depends on the temporal increase in morphogen signaling, the morphogen gradient scales with the growing tissue size, tissue growth becomes spatially uniform and the tissue naturally attains a finite size. This model is consistent with many properties of the wing disc. However, we find that the MDDR is not consistent with the phenotype of scaling-defective mutants, supporting the view that temporal increase in Dpp signaling is not the driver of cell division during late phases of disc development. More generally, our results show that local coupling of cell division with morphogen signaling can lead to gradient scaling and uniform growth even in the absence of global feedbacks. The MDDR scaling mechanism might be particularly beneficial during rapid proliferation, when global feedbacks are hard to implement.

Automated classification of cellular expression in multiplexed imaging data with Nimbus.

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference.

Dynamics of the cell-cycle network under genome-rewiring perturbations.

The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycle's promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein-DNA and protein-protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited 'resources' and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes, without a need for an optimal setting of the kinetic parameters.

Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA.

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data.

One snapshot of the peer review process for "Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data" (Geuenich et al., 2021).

Clump sequencing exposes the spatial expression programs of intestinal secretory cells.

Single-cell RNA sequencing combined with spatial information on landmark genes enables reconstruction of spatially-resolved tissue cell atlases. However, such approaches are challenging for rare cell types, since their mRNA contents are diluted in the spatial transcriptomics bulk measurements used for landmark gene detection. In the small intestine, enterocytes, the most common cell type, exhibit zonated expression programs along the crypt-villus axis, but zonation patterns of rare cell types such as goblet and tuft cells remain uncharacterized. Here, we present ClumpSeq, an approach for sequencing small clumps of attached cells. By inferring the crypt-villus location of each clump from enterocyte landmark genes, we establish spatial atlases for all epithelial cell types in the small intestine. We identify elevated expression of immune-modulatory genes in villus tip goblet and tuft cells and heterogeneous migration patterns of enteroendocrine cells. ClumpSeq can be applied for reconstructing spatial atlases of rare cell types in other tissues and tumors.

Spatial discordances between mRNAs and proteins in the intestinal epithelium.

The use of transcriptomes as reliable proxies for cellular proteomes is controversial. In the small intestine, enterocytes operate for 4 days as they migrate along villi, which are highly graded microenvironments. Spatial transcriptomics have demonstrated profound zonation in enterocyte gene expression, but how this variability translates to protein content is unclear. Here we show that enterocyte proteins and messenger RNAs along the villus axis are zonated, yet often spatially discordant. Using spatial sorting with zonated surface markers, together with a Bayesian approach to infer protein translation and degradation rates from the combined spatial profiles, we find that, while many genes exhibit proteins zonated toward the villus tip, mRNA is zonated toward the villus bottom. Finally, we demonstrate that space-independent protein synthesis delays can explain many of the mRNA-protein discordances. Our work provides a proteomic spatial blueprint of the intestinal epithelium, highlighting the importance of protein measurements for inferring cell states in tissues that operate outside of steady state.

Lgr5+ telocytes are a signaling source at the intestinal villus tip.

The intestinal epithelium is a structured organ composed of crypts harboring Lgr5+ stem cells, and villi harboring differentiated cells. Spatial transcriptomics have demonstrated profound zonation of epithelial gene expression along the villus axis, but the mechanisms shaping this spatial variability are unknown. Here, we combine laser capture micro-dissection and single cell RNA sequencing to uncover spatially zonated populations of mesenchymal cells along the crypt-villus axis. These include villus tip telocytes (VTTs) that express Lgr5, a gene previously considered a specific crypt epithelial stem cell marker. VTTs are elongated cells that line the villus tip epithelium and signal through Bmp morphogens and the non-canonical Wnt5a ligand. Their ablation is associated with perturbed zonation of enterocyte genes induced at the villus tip. Our study provides a spatially-resolved cell atlas of the small intestinal stroma and exposes Lgr5+ villus tip telocytes as regulators of the epithelial spatial expression programs along the villus axis.

A repressor-decay timer for robust temporal patterning in embryonic Drosophila neuroblast lineages.

Biological timers synchronize patterning processes during embryonic development. In the Drosophila embryo, neural progenitors (neuroblasts; NBs) produce a sequence of unique neurons whose identities depend on the sequential expression of temporal transcription factors (TTFs). The stereotypy and precision of NB lineages indicate reproducible TTF timer progression. We combine theory and experiments to define the timer mechanism. The TTF timer is commonly described as a relay of activators, but its regulatory circuit is also consistent with a repressor-decay timer, where TTF expression begins when its repressor decays. Theory shows that repressor-decay timers are more robust to parameter variations than activator-relay timers. This motivated us to experimentally compare the relative importance of the relay and decay interactions in vivo. Comparing WT and mutant NBs at high temporal resolution, we show that the TTF sequence progresses primarily by repressor-decay. We suggest that need for robust performance shapes the evolutionary-selected designs of biological circuits.

A WntD-Dependent Integral Feedback Loop Attenuates Variability in Drosophila Toll Signaling.

Patterning by morphogen gradients relies on the capacity to generate reproducible distribution profiles. Morphogen spread depends on kinetic parameters, including diffusion and degradation rates, which vary between embryos, raising the question of how variability is controlled. We examined this in the context of Toll-dependent dorsoventral (DV) patterning of the Drosophila embryo. We find that low embryo-to-embryo variability in DV patterning relies on wntD, a Toll-target gene expressed initially at the posterior pole. WntD protein is secreted and disperses in the extracellular milieu, associates with its receptor Frizzled4, and inhibits the Toll pathway by blocking the Toll extracellular domain. Mathematical modeling predicts that WntD accumulates until the Toll gradient narrows to its desired spread, and we support this feedback experimentally. This circuit exemplifies a broadly applicable induction-contraction mechanism, which reduces patterning variability through a restricted morphogen-dependent expression of a secreted diffusible inhibitor.