Single cell response landscape of graded Nodal signaling in zebrafish explants

Nodal, as a morphogen, plays important roles in cell fate decision, pattern formation and organizer function. But because of the complex context in vivo and technology limitations, systematic studying of genes, cell types and patterns induced by Nodal alone is still missing. Here, by using a relatively simplified model, the zebrafish blastula animal pole explant avoiding additional instructive signals and prepatterns, we constructed a single cell response landscape of graded Nodal signaling, identified 105 Nodal immediate targets and depicted their expression patterns. Our results show that Nodal signaling is sufficient to induce anterior-posterior patterned axial mesoderm and head structure. Surprisingly, the endoderm induced by Nodal alone is mainly the anterior endoderm which gives rise to the pharyngeal pouch only, but not internal organs. Among the 105 Nodal targets, we identified 14 genes carrying varying levels of axis induction capability. Overall, our work provides new insights for understanding of the Nodal function and a valuable resource for future studies of patterning and morphogenesis induced by it.

Compositional perturbation autoencoder for single-cell response modeling

Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret and prioritize perturbations. Here, we present the Compositional Perturbation Autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug response across different cell types, doses, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, allowing drug similarity analysis and predictions for unseen dosages and drug combinations. We show CPA accurately models single-cell perturbations across compounds, dosages, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying it captures features that distinguish different interaction programs. Finally, we demonstrate CPA allows in-silico generation of 5,329 missing combinations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design by enabling in silico response prediction at the single-cell level.

Visual response characteristics in lateral and medial subdivisions of the rat pulvinar

The pulvinar is a higher-order thalamic relay and a central component of the extrageniculate visual pathway, with input from the superior colliculus and visual cortex and output to all of visual cortex. Rodent pulvinar, more commonly called the lateral posterior nucleus (LP), consists of three highly-conserved subdivisions, and offers the advantage of simplicity in its study compared to more subdivided primate pulvinar. Little is known about receptive field properties of LP, let alone whether functional differences exist between different LP subdivisions, making it difficult to understand what visual information is relayed and what kinds of computations the pulvinar might support. Here, we characterized single-cell response properties in two V1 recipient subdivisions of rat pulvinar, the rostromedial (LPrm) and lateral (LPl), and found that a fourth of the cells were selective for orientation, compared to half in V1, and that LP tuning widths were significantly broader. Response latencies were also significantly longer and preferred size more than three times larger on average than in V1; the latter suggesting pulvinar as a source of spatial context to V1. Between subdivisons, LPl cells preferred higher temporal frequencies, whereas LPrm showed a greater degree of direction selectivity and pattern motion detection. Taken together with known differences in connectivity patterns, these results suggest two separate visual feature processing channels in the pulvinar, one in LPl related to higher speed processing which likely derives from superior colliculus input, and the other in LPrm for motion processing derived through input from visual cortex.Significance StatementThe pulvinar has a perplexing role in visual cognition as no clear link has been found between the functional properties of its neurons and behavioral deficits that arise when it is damaged. The pulvinar, called the lateral posterior nucleus (LP) in rats, is a higher order thalamic relay with input from the superior colliculus and visual cortex and output to all of visual cortex. By characterizing single-cell response properties in anatomically distinct subdivisions we found two separate visual feature processing channels in the pulvinar, one in lateral LP related to higher speed processing which likely derives from superior colliculus input, and the other in rostromedial LP for motion processing derived through input from visual cortex.

A distributed neural code in the dentate gyrus and in CA1

ABSTRACTThe tuning properties of neurons in a given brain region have been traditionally viewed as the under-pinnings of computation in neural circuits. However, at the higher levels of processing, specialization is often elusive, instead a mix of sensory, cognitive and behavioural quantities drive neural activity. In such networks, ensembles of neurons, rather than single units with easily interpretable tuning properties, encode behaviourally relevant variables. Here we show that this is the case also in the dentate gyrus and CA1 subregions of the hippocampus. Using calcium imaging in freely moving mice, we decoded the instantaneous position, direction of motion and speed from the activity of hundreds of cells in the hippocampus of mice freely exploring an arena. For the vast majority of neurons in both regions, their response properties were not predictive of their importance for encoding position. Furthermore, we could decode position from populations of cells that were important for decoding direction of motion and vice versa, showing that these quantities are encoded by largely overlapping ensembles as in distributed neural code. Finally, we found that correlated activities had an impact on decoding performance in CA1 but not in dentate gyrus, suggesting different enconding strategies for these areas. Our analysis indicates that classical methods of analysis based on single cell response properties might be insufficient to accurately characterize the neural computation in a given area. In contrast, population analysis may help highlight previously overlooked properties of hippocampal circuits.

Cellular heterogeneity mediates inherent sensitivity–specificity tradeoff in cancer targeting by synthetic circuits

Synthetic gene circuits are emerging as a versatile means to target cancer with enhanced specificity by combinatorial integration of multiple expression markers. Such circuits must also be tuned to be highly sensitive because escape of even a few cells might be detrimental. However, the error rates of decision-making circuits in light of cellular variability in gene expression have so far remained unexplored. Here, we measure the single-cell response function of a tunable logic AND gate acting on two promoters in heterogeneous cell populations. Our analysis reveals an inherent tradeoff between specificity and sensitivity that is controlled by the AND gate amplification gain and activation threshold. We implement a tumor-mimicking cell-culture model of cancer cells emerging in a background of normal ones, and show that molecular parameters of the synthetic circuits control specificity and sensitivity in a killing assay. This suggests that, beyond the inherent tradeoff, synthetic circuits operating in a heterogeneous environment could be optimized to efficiently target malignant state with minimal loss of specificity.