The growth and division of cells in plant leaves is highly dynamic in time and space, all while the cells cannot move relative to their neighbors. Given these constraints, models predict that long range regulatory systems must exist to maintain flat forms. Juxtaposed microRNA (miRNA) networks could serve as one of these regulatory systems. One of these miRNAs, miR319 is thought to be expressed from the base of leaves and to promote growth by degrading class II TCP transcription factor mRNAs. A miR319 overexpression mutant,jagged and wavy (jaw-D)exhibits rippling and undulating leaves, consistent with biomechanical predictions that without genetic spatial coordination, tissues will deform. It has been theorized thatjaw-Drippling results from overgrowth at the margins, however this does not fully address how miR319 expression from the base of wild-type (WT) leaves allows them to flatten. Here, we track the growth, cell division and cell maturation in live WT andjaw-Dleaves to ask how miR319 expression in WT promotes flattening. This data revealed the importance of spatially distinct growth, division and differentiation patterns in WT leaves, which are missing injaw-D. We propose that WT leaf cells respond to differentiation cues to dynamically re-orient growth in specific tissue locations and regulate flattening.
Motivation DNA barcodes are short, random nucleotide sequences introduced into cell populations to track the relative counts of hundreds of thousands of individual lineages over time. Lineage tracking is widely applied, e.g. to understand evolutionary dynamics in microbial populations and the progression of breast cancer in humans. Barcode sequences are unknown upon insertion and must be identified using next-generation sequencing technology, which is error prone. In this study, we frame the barcode error correction task as a clustering problem with the aim to identify true barcode sequences from noisy sequencing data. We present Shepherd, a novel clustering method that is based on an indexing system of barcode sequences using k-mers, and a Bayesian statistical test incorporating a substitution error rate to distinguish true from error sequences. Results When benchmarking with synthetic data, Shepherd provides barcode count estimates that are significantly more accurate than state-of-the-art methods, producing 10-150 times fewer spurious lineages. For empirical data, Shepherd produces results that are consistent with the improvements seen on synthetic data. These improvements enable higher resolution lineage tracking and more accurate estimates of biologically relevant quantities, e.g. the detection of small effect mutations. Availability A Python implementation of Shepherd is freely available at: https://www.github.com/Nik-Tavakolian/Shepherd. Supplementary information Supplementary data are available at Bioinformatics online.
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