In the mouse distal terminal phalanx (P3), it remains mystery why amputation at less than 33% of the digit results in regeneration, while amputation exceeding 67% leads to non-regeneration. Unraveling the molecular mechanisms underlying this disparity could provide crucial insights for regenerative medicine. In this study, we aim to investigate the tissues within the wound bed to understand the tissue microenvironment associated with regenerative versus non-regenerative outcomes. We employed a P3-specific amputation model in mice, integrated with time-series RNA-seq and a macrophage assay challenged with pro- and anti-inflammatory cytokines, to explore these mechanisms. Our findings revealed that non-regenerative digits exhibit a greater intense early transcriptional response in the wound bed compared to regenerative ones. Furthermore, early macrophage phenotypes differ distinctly between regenerative and non-regenerative outcomes. Regenerative digits also display unique co-expression modules related to Bone Morphogenetic Protein 2 (Bmp2). The differentially expressed genes (DEGs) between regenerative and non-regenerative digits are enriched in targets of several transcription factors, such as HOXA11 and HOXD11 from the HOX gene family, showing a time-dependent pattern of enrichment. These transcription factors, known for their roles in bone regeneration, skeletal patterning, osteoblast activity, fracture healing, angiogenesis, and key signaling pathways, may act as master regulators of the regenerative gene signatures. Additionally, we developed a deep learning AI model capable of predicting post-amputation time and level from RNA-seq data, indicating that the regenerative probability may be "encoded" in the transcriptomic response to amputation.

UNLABELLED: Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for profiling cell-to-cell variability in cell populations. However, the combination of technical noise and intrinsic biological variability makes detecting technical artifacts in scRNA-seq samples particularly challenging. Proper detection of technical artifacts is critical to prevent spurious results during downstream analysis. In this study, we present 'Single-cell RNA-seq Quality Control' (SinQC), a method and software tool to detect technical artifacts in scRNA-seq samples by integrating both gene expression patterns and data quality information. We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool for controlling scRNA-seq data quality. AVAILABILITY AND IMPLEMENTATION: SinQC software and documents are available at http://www.morgridge.net/SinQC.html CONTACTS: : [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Aptamers are 'synthetic antibodies' that can bind to target molecules with high affinity and specificity. Aptamers are chemically synthesized and their discovery can be performed completely in vitro, rather than relying on in vivo biological processes, making them well-suited for high-throughput discovery. However, a large fraction of the most enriched aptamers in Systematic Evolution of Ligands by EXponential enrichment (SELEX) rounds display poor binding activity. Here, we present MPBind, a Meta-motif-based statistical framework and pipeline to Predict the BIND: ing potential of SELEX-derived aptamers. Using human embryonic stem cell SELEX-Seq data, MPBind achieved high prediction accuracy for binding potential. Further analysis showed that MPBind is robust to both polymerase chain reaction amplification bias and incomplete sequencing of aptamer pools. These two biases usually confound aptamer analysis. AVAILABILITY AND IMPLEMENTATION: MPBind software and documents are available at http://www.morgridge.net/MPBind.html. The human embryonic stem cells whole-cell SELEX-Seq data are available at http://www.morgridge.net/Aptamer/.