Preservation of Chromatin Organization after Acute Loss of CTCF in Mouse Embryonic Stem Cells, bioRxiv, 2017-03-21

SummaryThe CCCTC-binding factor (CTCF) is widely regarded as a key player in chromosome organization in mammalian cells, yet direct assessment of the impact of loss of CTCF on genome architecture has been difficult due to its essential role in cell proliferation and early embryogenesis. Here, using auxin-inducible degron techniques to acutely deplete CTCF in mouse embryonic stem cells, we show that cell growth is severely slowed yet chromatin organization remains largely intact after loss of CTCF. Depletion of CTCF reduces interactions between chromatin loop anchors, diminishes occupancy of cohesin complex genome-wide, and slightly weakens topologically associating domain (TAD) structure, but the active and inactive chromatin compartments are maintained and the vast majority of TAD boundaries persist. Furthermore, transcriptional regulation and histone marks associated with enhancers are broadly unchanged upon CTCF depletion. Our results suggest CTCF-independent mechanisms in maintenance of chromatin organization.

The Drosophila Embryo at Single Cell Transcriptome Resolution, bioRxiv, 2017-03-18

ABSTRACTDrosophila is a premier model system for understanding the molecular mechanisms of development. By the onset of morphogenesis, ~6000 cells express distinct gene combinations according to embryonic position. Despite extensive mRNA in situ screens, combinatorial gene expression within individual cells is largely unknown. Therefore, it is difficult to comprehensively identify the coding and non-coding transcripts that drive patterning and to decipher the molecular basis of cellular identity. Here, we single-cell sequence precisely staged embryos, measuring >3100 genes per cell. We produce a ‘transcriptomic blueprint’ of development – a virtual embryo where 3D locations of sequenced cells are confidently identified. Our “Drosophila-Virtual-Expression-eXplorer” performs virtual in situ hybridizations and computes expression gradients. Using DVEX, we predict spatial expression and discover patterned lncRNAs. DEVX is sensitive enough to detect subtle evolutionary changes in expression patterns between Drosophila species. We believe DVEX is a prototype for powerful single cell studies in complex tissues.

Live tracking of Moving samples in confocal microscopy for vertically grown roots tips, bioRxiv, 2017-03-15

AbstractRoots navigate through soil integrating environmental signals to orient their growth. The Arabidopsis root is a widely used model for developmental, physiological and cell biological studies. Live imaging greatly aids these efforts, but the horizontal sample position and continuous root tip displacement present significant difficulties. Here, we develop a confocal microscope setup for vertical sample mounting and integrated directional illumination. We present TipTracker - a custom software for automatic tracking of diverse moving objects usable on various microscope setups. Combined, this enables observation of root tips growing along the natural gravity vector over prolonged periods of time, as well as the ability to induce rapid gravity or light stimulation. We also track migrating cells in the developing zebrafish embryo, demonstrating the utility of this system in the acquisition of high resolution data sets of dynamic samples. We provide detailed descriptions of the tools enabling the easy implementation on other microscopes.

New mutations, old statistical challenges, bioRxiv, 2017-03-13

DroNc-Seq Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq, bioRxiv, 2017-03-10

Single nucleus RNA-Seq (sNuc-Seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-Seq, massively parallel sNuc-Seq with droplet technology. We profile 29,543 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for charting systematic cell atlases.

Scaling up DNA data storage and random access retrieval, bioRxiv, 2017-03-08

Current storage technologies can no longer keep pace with exponentially growing amounts of data. 1 Synthetic DNA offers an attractive alternative due to its potential information density of ~ 1018 Bmm3, 107 times denser than magnetic tape, and potential durability of thousands of years.2 Recent advances in DNA data storage have highlighted technical challenges, in particular, coding and random access, but have stored only modest amounts of data in synthetic DNA. 3,4,5 This paper demonstrates an end-to-end approach toward the viability of DNA data storage with large-scale random access. We encoded and stored 35 distinct files, totaling 200MB of data, in more than 13 million DNA oligonucleotides (about 2 billion nucleotides in total) and fully recovered the data with no bit errors, representing an advance of almost an order of magnitude compared to prior work. 6 Our data curation focused on technologically advanced data types and historical relevance, including the Universal Declaration of Human Rights in over 100 languages,7 a high-definition music video of the band OK Go,8 and a CropTrust database of the seeds stored in the Svalbard Global Seed Vault.9 We developed a random access methodology based on selective amplification, for which we designed and validated a large library of primers, and successfully retrieved arbitrarily chosen items from a subset of our pool containing 10.3 million DNA sequences. Moreover, we developed a novel coding scheme that dramatically reduces the physical redundancy (sequencing read coverage) required for error-free decoding to a median of 5x, while maintaining levels of logical redundancy comparable to the best prior codes. We further stress-tested our coding approach by successfully decoding a file using the more error-prone nanopore-based sequencing. We provide a detailed analysis of errors in the process of writing, storing, and reading data from synthetic DNA at a large scale, which helps characterize DNA as a storage medium and justify our coding approach. Thus, we have demonstrated a significant improvement in data volume, random access, and encodingdecoding schemes that contribute to a whole-system vision for DNA data storage.