The Fraser complex interconnects tissue layers to support basal epidermis and osteoblast integrated morphogenesis underlying fin skeletal patterning.

Fraser Syndrome is a rare, multisystemic autosomal recessive disorder characterized by disrupted epithelial-mesenchymal associations upon loss of Fraser Complex genes. Disease manifestation and affected organs are highly variable. Digit malformations such as syndactyly are common but of unclear developmental origins. We explored if zebrafish fraser extracellular matrix complex subunit 1 (fras1) mutants model Fraser Syndrome-associated appendicular skeleton patterning defects. Approximately 10% of fras1 mutants survive to adulthood, displaying striking and varied fin abnormalities, including endochondral bone fusions, ectopic cartilage, and disrupted caudal fin symmetry. The fins of surviving fras1 mutants frequently have fewer and unbranched bony rays. fras1 mutant fins regenerate to their original size but with exacerbated ray branching and fin symmetry defects. Single cell RNA-Seq analysis, in situ hybridizations, and antibody staining show specific Fraser complex expression in the basal epidermis during regenerative outgrowth. Fras1 and Fraser Complex component Frem2 accumulate along the basal side of distal-most basal epidermal cells. Greatly reduced and mislocalized Frem2 accompanies loss of Fras1 in fras1 mutants. The Sonic hedgehog signaling between distal basal epidermis and adjacent mesenchymal pre-osteoblasts that promotes ray branching persists upon Fraser Complex loss. However, fras1 mutant regenerating fins exhibit extensive sub-epidermal blistering associated with a disorganized basal epidermis and adjacent pre-osteoblasts. We propose Fraser Complex-supported tissue layer adhesion enables robust integrated tissue morphogenesis involving the basal epidermis and osteoblasts. Further, we establish zebrafish fin development and regeneration as an accessible model to explore mechanisms of Fraser Syndrome-associated digit defects and Fraser Complex function at epithelial-mesenchymal interfaces.

Single-cell transcriptome reveals insights into the development and function of the zebrafish ovary.

Zebrafish are an established research organism that has made many contributions to our understanding of vertebrate tissue and organ development, yet there are still significant gaps in our understanding of the genes that regulate gonad development, sex, and reproduction. Unlike the development of many organs, such as the brain and heart that form during the first few days of development, zebrafish gonads do not begin to form until the larval stage (≥5 days post-fertilization). Thus, forward genetic screens have identified very few genes required for gonad development. In addition, bulk RNA-sequencing studies that identify genes expressed in the gonads do not have the resolution necessary to define minor cell populations that may play significant roles in the development and function of these organs. To overcome these limitations, we have used single-cell RNA sequencing to determine the transcriptomes of cells isolated from juvenile zebrafish ovaries. This resulted in the profiles of 10,658 germ cells and 14,431 somatic cells. Our germ cell data represents all developmental stages from germline stem cells to early meiotic oocytes. Our somatic cell data represents all known somatic cell types, including follicle cells, theca cells, and ovarian stromal cells. Further analysis revealed an unexpected number of cell subpopulations within these broadly defined cell types. To further define their functional significance, we determined the location of these cell subpopulations within the ovary. Finally, we used gene knockout experiments to determine the roles of foxl2l and wnt9b for oocyte development and sex determination and/or differentiation, respectively. Our results reveal novel insights into zebrafish ovarian development and function, and the transcriptome profiles will provide a valuable resource for future studies.

Achieving the shot-noise limit using experimental multi-shot digital holography data.

In this paper, we achieve the shot-noise limit using straightforward image-post-processing techniques with experimental multi-shot digital holography data (i.e., off-axis data composed of multiple noise and speckle realizations). First, we quantify the effects of frame subtraction (of the mean reference-only frame and the mean signal-only frame from the digital-hologram frames), which boosts the signal-to-noise ratio (SNR) of the baseline dataset with a gain of 2.4 dB. Next, we quantify the effects of frame averaging, both with and without the frame subtraction. We show that even though the frame averaging boosts the SNR by itself, the frame subtraction and the stability of the digital-hologram fringes are necessary to achieve the shot-noise limit. Overall, we boost the SNR of the baseline dataset with a gain of 8.1 dB, which is the gain needed to achieve the shot-noise limit.

Imaging through deep turbulence using single-shot digital holography data.

This paper uses an experimental setup consisting of phase plates and a digital-holography receiver to validate the performance of an algorithm, referred to as multi-plane iterative reconstruction (MIR), for imaging through deep turbulence. In general, deep-turbulence conditions arise from aberrations being distributed along the propagation path. The resulting phase errors then cause a multifaceted problem with multiple empirically determined limitations. To address these limitations, the MIR algorithm works by sensing and correcting for the distributed-volume phase errors using single-shot digital holography data (i.e., one speckle measurement from the coherent illumination of an optically rough extended object). As such, we first show that our distributed-volume phase errors, created using the phase plates, follow path-integrated Kolmogorov statistics for weak-to-deep turbulence strengths. We then present results from two MIR algorithm configurations: a) where we have a priori knowledge of the placement of the phase plates, so that we sense and correct in the exact locations of the phase errors, and b) where we do not have a priori knowledge of the placement of the phase plates, so that we sense and correct in two fixed planes for all phase-error combinations. Given weak-to-deep turbulence strengths, the results show that the two MIR algorithm configurations perform comparably for the four imaging scenarios tested. Such results are promising for tactical applications, where one might not have a priori knowledge of the deep-turbulence conditions.