Self-organized BMP signaling dynamics underlie the development and evolution of digit segmentation patterns in birds and mammals.

Repeating patterns of synovial joints are a highly conserved feature of articulated digits, with variations in joint number and location resulting in diverse digit morphologies and limb functions across the tetrapod clade. During the development of the amniote limb, joints form iteratively within the growing digit ray, as a population of distal progenitors alternately specifies joint and phalanx cell fates to segment the digit into distinct elements. While numerous molecular pathways have been implicated in this fate choice, it remains unclear how they give rise to a repeating pattern. Here, using single-cell RNA sequencing and spatial gene expression profiling, we investigate the transcriptional dynamics of interphalangeal joint specification in vivo. Combined with mathematical modeling, we predict that interactions within the BMP signaling pathway-between the ligand GDF5, the inhibitor NOGGIN, and the intracellular effector pSMAD-result in a self-organizing Turing system that forms periodic joint patterns. Our model is able to recapitulate the spatiotemporal gene expression dynamics observed in vivo, as well as phenocopy digit malformations caused by BMP pathway perturbations. By contrasting in silico simulations with in vivo morphometrics of two morphologically distinct digits, we show how changes in signaling parameters and growth dynamics can result in variations in the size and number of phalanges. Together, our results reveal a self-organizing mechanism that underpins amniote digit segmentation and its evolvability and, more broadly, illustrate how Turing systems based on a single molecular pathway may generate complex repetitive patterns in a wide variety of organisms.

A method to investigate muscle target-specific transcriptional signatures of single motor neurons.

BACKGROUND: Motor neurons in the vertebrate spinal cord have long served as a paradigm to study the transcriptional logic of cell type specification and differentiation. At limb levels, pool-specific transcriptional signatures first restrict innervation to only one particular muscle in the periphery, and get refined, once muscle connection has been established. Accordingly, to study the transcriptional dynamics and specificity of the system, a method for establishing muscle target-specific motor neuron transcriptomes would be required. RESULTS: To investigate target-specific transcriptional signatures of single motor neurons, here we combine ex-ovo retrograde axonal labeling in mid-gestation chicken embryos with manual isolation of individual fluorescent cells and Smart-seq2 single-cell RNA-sequencing. We validate our method by injecting the dorsal extensor metacarpi radialis and ventral flexor digiti quarti wing muscles and harvesting a total of 50 fluorescently labeled cells, in which we detect up to 12,000 transcribed genes. Additionally, we present visual cues and cDNA metrics predictive of sequencing success. CONCLUSIONS: Our method provides a unique approach to study muscle target-specific motor neuron transcriptomes at a single-cell resolution. We anticipate that our method will provide key insights into the transcriptional logic underlying motor neuron pool specialization and proper neuromuscular circuit assembly and refinement.

A single-cell transcriptomic atlas of the developing chicken limb.

BACKGROUND: Through precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades' worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution. RESULTS: Using single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits. CONCLUSIONS: We provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.

Extracellular matrix gene expression signatures as cell type and cell state identifiers.

Transcriptomic signatures based on cellular mRNA expression profiles can be used to categorize cell types and states. Yet whether different functional groups of genes perform better or worse in this process remains largely unexplored. Here we test the core matrisome - that is, all genes coding for structural proteins of the extracellular matrix - for its ability to delineate distinct cell types in embryonic single-cell RNA-sequencing (scRNA-seq) data. We show that even though expressed core matrisome genes correspond to less than 2% of an entire cellular transcriptome, their RNA expression levels suffice to recapitulate essential aspects of cell type-specific clustering. Notably, using scRNA-seq data from the embryonic limb, we demonstrate that core matrisome gene expression outperforms random gene subsets of similar sizes and can match and exceed the predictive power of transcription factors. While transcription factor signatures generally perform better in predicting cell types at early stages of chicken and mouse limb development, i.e., when cells are less differentiated, the information content of the core matrisome signature increases in more differentiated cells. Moreover, using cross-species analyses, we show that these cell type-specific signatures are evolutionarily conserved. Our findings suggest that each cell type produces its own unique extracellular matrix, or matreotype, which becomes progressively more refined and cell type-specific as embryonic tissues mature.