No democracy, no academia.

Over the past 30 weeks, Israel has been undergoing an upheaval marked by unprecedented attacks by the government on the independence of its judiciary, attorney general, government legal advisers, police, military, public broadcasting, and religious freedom. This assault on democratic institutions and principles is an imminent threat to Israeli academia, which relies on a solid democratic foundation. In response, universities, academics, and students have emerged as key proponents of ongoing protests under the banner, "No democracy, no academia."

Whole-Genome Amplification-Surveying Yield, Reproducibility, and Heterozygous Balance, Reported by STR-Targeting MIPs.

Whole-genome amplification is a crucial first step in nearly all single-cell genomic analyses, with the following steps focused on its products. Bias and variance caused by the whole-genome amplification process add numerous challenges to the world of single-cell genomics. Short tandem repeats are sensitive genomic markers used widely in population genetics, forensics, and retrospective lineage tracing. A previous evaluation of common whole-genome amplification targeting ~1000 non-autosomal short tandem repeat loci is extended here to ~12,000 loci across the entire genome via duplex molecular inversion probes. Other than its improved scale and reduced noise, this system detects an abundance of heterogeneous short tandem repeat loci, allowing the allelic balance to be reported. We show here that while the best overall yield is obtained using RepliG-SC, the maximum uniformity between alleles and reproducibility across cells are maximized by Ampli1, rendering it the best candidate for the comparative heterozygous analysis of single-cell genomes.

Efficient acquisition of tens of thousands of short tandem repeats in single-cell whole-genome-amplified DNA.

Short tandem repeats (STRs) are highly abundant in the human genome, but existing approaches for accurate genotyping of STRs are limited. Here, we describe a protocol for duplex molecular inversion probes for high-throughput and cost-effective STR enrichment. We have successfully tested panels targeting as many as 50K STRs in several thousands of genomic samples (e.g., HeLa cells, Du145 cells, leukemia cells, melanoma cells). However, because the protocol is plate based, the sample size is limited to a few thousand. For complete details on the use and execution of this protocol, please refer to Tao et al. (2021).

Retrospective cell lineage reconstruction in humans by using short tandem repeats.

Cell lineage analysis aims to uncover the developmental history of an organism back to its cell of origin. Recently, novel in vivo methods utilizing genome editing enabled important insights into the cell lineages of animals. In contrast, human cell lineage remains restricted to retrospective approaches, which still lack resolution and cost-efficient solutions. Here, we demonstrate a scalable platform based on short tandem repeats targeted by duplex molecular inversion probes. With this human cell lineage tracing method, we accurately reproduced a known lineage of DU145 cells and reconstructed lineages of healthy and metastatic single cells from a melanoma patient who matched the anatomical reference while adding further refinements. This platform allowed us to faithfully recapitulate lineages of developmental tissue formation in healthy cells. In summary, our lineage discovery platform can profile informative somatic mutations efficiently and provides solid lineage reconstructions even in challenging low-mutation-rate healthy single cells.

Comparison of seven single cell whole genome amplification commercial kits using targeted sequencing.

Advances in whole genome amplification (WGA) techniques enable understanding of the genomic sequence at a single cell level. Demand for single cell dedicated WGA kits (scWGA) has led to the development of several commercial kit. To this point, no robust comparison of all available kits was performed. Here, we benchmark an economical assay, comparing all commercially available scWGA kits. Our comparison is based on targeted sequencing of thousands of genomic loci, including highly mutable regions, from a large cohort of human single cells. Using this approach we have demonstrated the superiority of Ampli1 in genome coverage and of RepliG in reduced error rate. In summary, we show that no single kit is optimal across all categories, highlighting the need for a dedicated kit selection in accordance with experimental requirements.

Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees.

The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.

Short tandem repeat stutter model inferred from direct measurement of in vitro stutter noise.

Short tandem repeats (STRs) are polymorphic genomic loci valuable for various applications such as research, diagnostics and forensics. However, their polymorphic nature also introduces noise during in vitro amplification, making them difficult to analyze. Although it is possible to overcome stutter noise by using amplification-free library preparation, such protocols are presently incompatible with single cell analysis and with targeted-enrichment protocols. To address this challenge, we have designed a method for direct measurement of in vitro noise. Using a synthetic STR sequencing library, we have calibrated a Markov model for the prediction of stutter patterns at any amplification cycle. By employing this model, we have managed to genotype accurately cases of severe amplification bias, and biallelic STR signals, and validated our model for several high-fidelity PCR enzymes. Finally, we compared this model in the context of a naïve STR genotyping strategy against the state-of-the-art on a benchmark of single cells, demonstrating superior accuracy.

On the journey from nematode to human, scientists dive by the zebrafish cell lineage tree.

Three recent single-cell papers use novel CRISPR-Cas9-sgRNA genome editing methods to shed light on the zebrafish cell lineage tree.

The Human Cell Atlas.

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

A generic, cost-effective, and scalable cell lineage analysis platform.

Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.

Accuracy of Answers to Cell Lineage Questions Depends on Single-Cell Genomics Data Quality and Quantity.

Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task.

eSTGt: a programming and simulation environment for population dynamics.

BACKGROUND: We have previously presented a formal language for describing population dynamics based on environment-dependent Stochastic Tree Grammars (eSTG). The language captures in broad terms the effect of the changing environment while abstracting away details on interaction among individuals. An eSTG program consists of a set of stochastic tree grammar transition rules that are context-free. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count and elapsed time. In addition, each individual may have an internal state, which can change during transitions. RESULTS: This paper presents eSTGt (eSTG tool), an eSTG programming and simulation environment. When executing a program, the tool generates the corresponding lineage trees as well as the internal states values, which can then be analyzed either through the tool's GUI or using MATLAB's command-line environment. CONCLUSIONS: The presented tool allows researchers to use existing biological knowledge in order to model the dynamics of a developmental process and analyze its behavior throughout the historical events. Simulated lineage trees can be used to validate various hypotheses in silico and to predict the behavior of dynamical systems under various conditions. Written under MATLAB environment, the tool also enables to easily integrate the output data within the user's downstream analysis.

Synthesis and cell-free cloning of DNA libraries using programmable microfluidics.

Microfluidics may revolutionize our ability to write synthetic DNA by addressing several fundamental limitations associated with generating novel genetic constructs. Here we report the first de novo synthesis and cell-free cloning of custom DNA libraries in sub-microliter reaction droplets using programmable digital microfluidics. Specifically, we developed Programmable Order Polymerization (POP), Microfluidic Combinatorial Assembly of DNA (M-CAD) and Microfluidic In-vitro Cloning (MIC) and applied them to de novo synthesis, combinatorial assembly and cell-free cloning of genes, respectively. Proof-of-concept for these methods was demonstrated by programming an autonomous microfluidic system to construct and clone libraries of yeast ribosome binding sites and bacterial Azurine, which were then retrieved in individual droplets and validated. The ability to rapidly and robustly generate designer DNA molecules in an autonomous manner should have wide application in biological research and development.

Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants.

Deducing generic causal relations between RNA transcript features and protein expression profiles from endogenous gene expression data remains a major unsolved problem in biology. The analysis of gene expression from heterologous genes contributes significantly to solving this problem, but has been heavily biased toward the study of the effect of 5' transcript regions and to prokaryotes. Here, we employ a synthetic biology driven approach that systematically differentiates the effect of different regions of the transcript on gene expression up to 240 nucleotides into the ORF. This enabled us to discover new causal effects between features in previously unexplored regions of transcripts, and gene expression in natural regimes. We rationally designed, constructed, and analyzed 383 gene variants of the viral HRSVgp04 gene ORF, with multiple synonymous mutations at key positions along the transcript in the eukaryote S. cerevisiae. Our results show that a few silent mutations at the 5'UTR can have a dramatic effect of up to 15 fold change on protein levels, and that even synonymous mutations in positions more than 120 nucleotides downstream from the ORF 5'end can modulate protein levels up to 160%-300%. We demonstrate that the correlation between protein levels and folding energy increases with the significance of the level of selection of the latter in endogenous genes, reinforcing the notion that selection for folding strength in different parts of the ORF is related to translation regulation. Our measured protein abundance correlates notably(correlation up to r = 0.62 (p=0.0013)) with mean relative codon decoding times, based on ribosomal densities (Ribo-Seq) in endogenous genes, supporting the conjecture that translation elongation and adaptation to the tRNA pool can modify protein levels in a causal/direct manner. This report provides an improved understanding of transcript evolution, design principles of gene expression regulation, and suggests simple rules for engineering synthetic gene expression in eukaryotes.

Lineage grammars: describing, simulating and analyzing population dynamics.

BACKGROUND: Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time. RESULTS: We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals. CONCLUSIONS: We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as "black boxes" in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.

Accurate, model-based tuning of synthetic gene expression using introns in S. cerevisiae.

Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.

Heuristic for maximizing DNA reuse in synthetic DNA library assembly.

De novo DNA synthesis is in need of new ideas for increasing production rate and reducing cost. DNA reuse in combinatorial library construction is one such idea. Here, we describe an algorithm for planning multistage assembly of DNA libraries with shared intermediates that greedily attempts to maximize DNA reuse, and show both theoretically and empirically that it runs in linear time. We compare solution quality and algorithmic performance to the best results reported for computing DNA assembly graphs, finding that our algorithm achieves solutions of equivalent quality but with dramatically shorter running times and substantially improved scalability. We also show that the related computational problem bounded-depth min-cost string production (BDMSP), which captures DNA library assembly operations with a simplified cost model, is NP-hard and APX-hard by reduction from vertex cover. The algorithm presented here provides solutions of near-minimal stages and thanks to almost instantaneous planning of DNA libraries it can be used as a metric of "manufacturability" to guide DNA library design. Rapid planning remains applicable even for DNA library sizes vastly exceeding today's biochemical assembly methods, future-proofing our method.

Correcting the bias against interdisciplinary research.

When making decisions about funding and jobs the scientific community should recognise that most of the tools used to evaluate scientific excellence are biased in favour of established disciplines and against interdisciplinary research.

Single cell analysis exposes intratumor heterogeneity and suggests that FLT3-ITD is a late event in leukemogenesis.

FMS-like tyrosine kinase 3 receptor-internal tandem duplication (FLT3-ITD) commonly occurs in acute myeloid leukemia and is considered rare in acute lymphocytic leukemia. Acute leukemia has poor prognosis, mainly due to relapse. Standard FLT3-ITD diagnostic techniques are based on genomic polymerase chain reaction and have recently incorporated GeneScan (Applied Biosystems, Foster City, CA) to identify variations of the FLT3 gene. As this is an average-based assay utilized in a heterogeneous leukemic cell population, we hypothesized that cells of acute leukemia, considered FLT3-ITD-negative by standard methods, could possess a fraction of FLT3-ITD-positive cells. The present study employed single cell mutation analysis to evaluate the FLT3-ITD status in newly diagnosed acute myeloid leukemia (n = 5) and acute lymphocytic leukemia (n = 3) patients. A total of 541 single leukemic cells and 36 mononuclear cells from healthy volunteers were analyzed. Seven patients, considered FLT3-ITD-negative according to bulk DNA analysis, appeared to possess a small fraction of FLT3-ITD-positive cells based on single cell analysis. Moreover, this approach revealed the heterogeneity of the tumor as evident by different FLT3-ITD mutations present in the same patient. The presence of a minor clone carrying FLT3-ITD in almost all patients tested provides evidence that this lesion is a common late event in leukemogenesis. Additionally, 3 relapsed patients demonstrated loss of heterozygosity of the normal allele, affecting 25%-100% of the cells found to be FLT3-ITD-positive. Though further clinical testing is warranted, these findings may have implications on the prognostic significance of FLT3-ITD and the use of targeted therapy.