Leon Anavy

Cultured meat presents a sustainable alternative to traditional meat production but faces significant challenges in scalability and cost efficiency. A key limitation is the restricted proliferation capacity of bovine mesenchymal stem cells (bMSCs), a widely used cell source in the field. Using a pooled, lentiviral CRISPR knockout screen, we interrogated 3000 CRISPR guides targeting 600 genes involved in stem cell regulation or proliferation. Notably, knockouts of TP53 and PTEN significantly increased proliferation rates and delayed senescence. Validation with individual gene knockouts confirms these effects, showing enhanced growth but reduced differentiation potential. We also identified chondrogenic differentiation as a promising target whose repression may further promote MSC expansion. These findings demonstrate the utility of CRISPR screening for optimizing bovine stem cell traits and offer a path toward more scalable cultured meat production in the future.

This study introduces a novel model for analyzing and determining the required sequencing coverage in DNA-based data storage, focusing on combinatorial DNA encoding. We seek to characterize the distribution of the number of sequencing reads required for message reconstruction. We use a variant of the coupon collector distribution for this purpose. For any given number of observed reads, R ϵ N, we use a Markov Chain representation of the process to compute the probability of error-free reconstruction. We develop theoretical bounds on the decoding probability and use empirical simulations to validate these bounds and assess tightness. This work contributes to understanding sequencing coverage in DNA-based data storage, offering insights into decoding complexity, error correction, and sequence reconstruction. We provide a Python package, with its input being the code design and other message parameters, all of which are denoted as Θ, and a desired confidence level 1-δ. This package computes the required read coverage, guaranteeing the message reconstruction R=R(δ,Θ).

This study introduces a novel model for analyzing and determining the required sequencing coverage in DNA-based data storage, focusing on combinatorial DNA encoding. We explore the application of the coupon collector model for combinatorial-letter reconstruction, post-sequencing, which ensure efficient data retrieval and error reduction. We use a Markov Chain model to compute the probability of error-free reconstruction. We develop theoretical bounds on the decoding probability and use empirical simulations to validate these bounds. The work contributes to the understanding of sequencing coverage in DNA-based data storage, offering insights into decoding complexity, error correction, and sequence reconstruction. We provide a Python package that takes the code design and other message parameters as input, and then computes the required read coverage to guarantee reconstruction at a given desired confidence.

Using synthetic DNA for data storage and for physical information encoding in labeling, tracing, and authentication applications is becoming more feasible as synthesis and reading technologies are improving. DNA in data storage applications has several advantages such as very high physical density and robustness. Some of the new synthesis technologies lead to repetition noise, consisting of sticky insertions and deletions in the resulting messages. In this paper, we address reconstruction algorithms for multiple trace communication channels with repetition (sticky insertion and deletion) noise. We prove correctness and analyze failure rates, both analytically and on simulated data. We identify a failure mechanism related to alternating stretches in the design sequence that leads to a potential bias in the data derived from reads (traces) and used for reconstruction. To minimize this effect we introduce alternating length limited codes (ALL codes) and analyze some of their properties.

1 With the world generating digital data at an exponential rate, DNA has emerged as a promising archival medium. It offers a more efficient and long-lasting digital storage solution due to its durability, physical density, and high information capacity. Research in the field includes the development of encoding schemes, which are compatible with existing DNA synthesis and sequencing technologies. Recent studies suggest leveraging the inherent information redundancy of these technologies by using composite DNA alphabets. A major challenge in this approach involves the noisy inference process, which prevented the use of large composite alphabets. This paper introduces a novel approach for DNA-based data storage, offering a 6.5-fold increase in logical density over standard DNA-based storage systems, with near zero reconstruction error. Combinatorial DNA encoding uses a set of clearly distinguishable DNA shortmers to construct large combinatorial alphabets, where each letter represents a subset of shortmers. The nature of these combinatorial alphabets minimizes mix-up errors, while also ensuring the robustness of the system. As this paper will show, we formally define various combinatorial encoding schemes and investigate their theoretical properties, such as information density, reconstruction probabilities and required synthesis, and sequencing multiplicities. We then suggest an end-to-end design for a combinatorial DNA-based data storage system, including encoding schemes, two-dimensional error correction codes, and reconstruction algorithms. Using in silico simulations, we demonstrate our suggested approach and evaluate different combinatorial alphabets for encoding 10KB messages under different error regimes. The simulations reveal vital insights, including the relative manageability of nucleotide substitution errors over shortmer-level insertions and deletions. Sequencing coverage was found to be a key factor affecting the system performance, and the use of two-dimensional Reed-Solomon (RS) error correction has significantly improved reconstruction rates. Our experimental proof-of-concept validates the feasibility of our approach, by constructing two combinatorial sequences using Gibson assembly imitating a 4-cycle combinatorial synthesis process. We confirmed the successful reconstruction, and established the robustness of our approach for different error types. Subsampling experiments supported the important role of sampling rate and its effect on the overall performance. Our work demonstrates the potential of combinatorial shortmer encoding for DNA-based data storage, while raising theoretical research questions and technical challenges. These include the development of error correction codes for combinatorial DNA, the exploration of optimal sampling rates, and the advancement of DNA synthesis technologies that support combinatorial synthesis. Combining combinatorial principles with error-correcting strategies paves the way for efficient, error-resilient DNA-based storage solutions.

Motivation: Recent years have seen a growing number and an expanding scope of studies using synthetic oligo libraries for a range of applications in synthetic biology. As experiments are growing by numbers and complexity, analysis tools can facilitate quality control and support better assessment and inference. Results: We present a novel analysis tool, called SOLQC, which enables fast and comprehensive analysis of synthetic oligo libraries, based on NGS analysis performed by the user. SOLQC provides statistical information such as the distribution of variant representation, different error rates and their dependence on sequence or library properties. SOLQC produces graphical reports from the analysis, in a flexible format. We demonstrate SOLQC by analyzing literature libraries. We also discuss the potential benefits and relevance of the different components of the analysis. Availability and implementation: SOLQC is a free software for non-commercial use, available at https://app.gitbook.com/@yoav-orlev/s/solqc/. For commercial use please contact the authors. Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: Nature provides a rich toolbox of dynamic nucleic acid structures that are widespread in cells and affect multiple biological processes¹. Recently, non-canonical structures gained renewed scientific and biotechnological interest^2,3. One particularly intriguing form of such structures are triplexes⁴in which a single-stranded nucleic acid molecule interacts via Hoogsteen bonds with a DNA/RNA double helix⁵. Despite extensive researchin vitro^6–9, the underlying rules for triplex formation remain debated and evidence for triplexesin vivois circumstantial^10–12. Here, we demonstrate the development of a deep-sequencing platform termed Triplex-Seq to systematically refine the DNA triplex code and identify high affinity triplex forming oligo (TFO) variants. We identified a preference for short G-rich motifs using an oligo-library with a mix of all four bases. These high-information content motifs formed specific high-affinity triplexes in a pH-independent manner and stability was increased with G-rich double-stranded molecules. We then conjugated one high-affinity and one low-affinity variant to a VP48 peptide and studied these synthetic biomolecules in mammalian cells. Using these peptide-oligo constructs (POCs), we demonstrated possible triplex-induced down-regulation activity in 544 differentially expressed genes. Our results show that deep-sequencing platforms can substantially expand our understanding of triplex binding rules, which in turn has led to the development of a functional non-genetically encoded regulatory tool forin vivoapplications.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Recent developments in personalized medicine are based on molecular measurement steps that guide personally adjusted medical decisions. A central approach to molecular profiling consists of measuring DNA, RNA, and/or proteins in tissue samples, most notably in and around tumors. This measurement yields molecular biomarkers that are potentially predictive of response and of tumor type. Current methods in cancer therapy mostly use tissue biopsy as the starting point of molecular profiling. Tissue biopsies involve a physical resection of a small tissue sample, leading to localized tissue injury, bleeding, inflammation and stress, as well as to an increased risk of metastasis. Here we developed a technology for harvesting biomolecules from tissues using electroporation. We show that tissue electroporation, achieved using a combination of high-voltage short pulses, 50 pulses 500 V cm⁻¹, 30 µs, 1 Hz, with low-voltage long pulses 50 pulses 50 V cm⁻¹, 10 ms, delivered at 1 Hz, allows for tissue-specific extraction of RNA and proteins. We specifically tested RNA and protein extraction from excised kidney and liver samples and from excised HepG2 tumors in mice. Further in vivo development of extraction methods based on electroporation can drive novel approaches to the molecular profiling of tumors and of tumor environment and to related diagnosis practices.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.