Random clonal expansion as a limiting factor in transplantable in vivo CRISPR/Cas9 screens

Transplantable in vivo CRISPR/Cas9 knockout screens, in which cells are transduced in vitro and inoculated into mice to form tumours in vivo, offer the opportunity to evaluate gene function in a cancer model that incorporates the multicellular interactions of the tumour microenvironment. In this study, we sought to develop a head and neck squamous cell carcinoma (HNSCC) tumour xenograft model for whole-genome screens that could maintain high gRNA representation during tumour initiation and progression. To achieve this, we sought early-passage HNSCC cell lines with a high frequency of tumour initiation-cells, and identified the pseudodiploid UT-SCC-54C line as a suitable model from 23 HNSCC lines tested based on a low tumourigenic dose for 50% takes (TD50) of 1100 cells in NSG mice. On transduction with the GeCKOv2 whole-genome gRNA library (119,461 unique gRNAs), high (80-95%) gRNA representation was maintained in early (up to 14 d) UT-SCC-54C tumours in NSG mice, but not in UT-SCC-74B tumours (TD50=9200). However, loss of gRNA representation was observed in UT-SCC-54C tumours following growth for 38-43 days, which correlated with a large increase in bias among gRNA read counts due to stochastic expansion of clones in the tumours. Applying binomial thinning simulations revealed that the UT-SCC-54C model would have 40-90% statistical power to detect drug sensitivity genes with log2 fold change effect sizes of 1-2 in early tumours with gRNA libraries of up to 10,000 gRNAs and modest group sizes of 5 tumours. In large tumours, this model would have had 45% power to detect log2 fold change effect sizes of 2-3 with libraries of 2,000 gRNAs and 14 tumours per group. Based on our findings, we conclude that gRNA library size, sample size and tumour size are all parameters that can be individually optimised to ensure transplantable in vivo CRISPR screens can successfully evaluate gene function.

Enhancing diversity analysis by repeatedly rarefying next generation sequencing data describing microbial communities

Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample’s library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

Fiction readers' advisory services in New Zealand public libraries : an investigation into how personal reading habits and other factors affect the confidence of library staff who answer adult or young adult fiction readers' advisory queries

<p>Fiction readers' advisory is the act of assisting a library borrower to find their next piece of fiction to read for leisure purposes. This is a significant part of public library work for staff members who work face-to-face with library borrowers. The confidence of library staff members who provide these services is therefore an important issue. It was hypothesised that a number of factors would influence the confidence of staff who answer fiction readers' advisory enquiries, including the amount and kind of pleasure reading undertaken, library size, years of library experience, and training. An online questionnaire was used to survey a sample of frontline public library staff members in New Zealand to gather data about readers' advisory services in public libraries in this country, as very little research has been undertaken here in this area. There was an excellent response to the survey invitation, and 130 completed questionnaires were received. Survey respondents' confidence about answering fiction readers' advisory enquiries was positively correlated with several factors, including amount and breadth of personal reading, length of public library service, kind of training received, and number of readers' advisory tools available. Negative correlations were suggested between library qualifications and confidence, and library size and confidence. Other factors which may influence confidence were also identified, such as library staff morale, having sufficient time for answering enquiries properly, and time for pleasure reading.</p>

Fiction readers' advisory services in New Zealand public libraries : an investigation into how personal reading habits and other factors affect the confidence of library staff who answer adult or young adult fiction readers' advisory queries

<p>Fiction readers' advisory is the act of assisting a library borrower to find their next piece of fiction to read for leisure purposes. This is a significant part of public library work for staff members who work face-to-face with library borrowers. The confidence of library staff members who provide these services is therefore an important issue. It was hypothesised that a number of factors would influence the confidence of staff who answer fiction readers' advisory enquiries, including the amount and kind of pleasure reading undertaken, library size, years of library experience, and training. An online questionnaire was used to survey a sample of frontline public library staff members in New Zealand to gather data about readers' advisory services in public libraries in this country, as very little research has been undertaken here in this area. There was an excellent response to the survey invitation, and 130 completed questionnaires were received. Survey respondents' confidence about answering fiction readers' advisory enquiries was positively correlated with several factors, including amount and breadth of personal reading, length of public library service, kind of training received, and number of readers' advisory tools available. Negative correlations were suggested between library qualifications and confidence, and library size and confidence. Other factors which may influence confidence were also identified, such as library staff morale, having sufficient time for answering enquiries properly, and time for pleasure reading.</p>

Normalization of Gene Expression Data Revisited: The Three Viewpoints of the Transcriptome in Human Skeletal Muscle Undergoing Load-induced Hypertrophy and Why They Matter

BackgroundThe biological relevance and accuracy of gene expression data depend on the adequacy of data normalization. This is both due to its role in resolving and accounting for technical variation and errors, and its defining role in shaping the viewpoint of biological interpretations. Still, normalization is often treated in serendipitous manners. This is especially true for the viewpoint perspective, which may be particularly decisive for conclusions in studies involving pronounced cellular plasticity. In this study, we highlight the consequences of using three fundamentally different modes of normalization for interpreting RNA-seq data from human skeletal muscle undergoing exercise-training-induced growth. Briefly, 25 participants conducted 12 weeks of high-load resistance training. Muscle biopsy specimens were sampled from m. vastus lateralis before, after two weeks of training (week 2) and after the intervention (week 12), and were subsequently analyzed using RNA-seq. Transcript counts were modeled as i) per-library-size, ii) per-total-RNA, and iii) per-sample-size (per-mg-tissue). ResultInitially, the three modes of transcript modeling led to the identification of three unique sets of stable genes, which displayed differential expression profiles. Specifically, genes showing stable expression across samples in the per-library-size dataset displayed training-associated increases in per-total-RNA and per-sample-size datasets. These gene sets were then used for normalization of the entire dataset, providing transcript abundance estimates corresponding to each of the three biological viewpoints (i.e., per-library-size, per-total-RNA, and per-sample-size). The different normalization modes led to different conclusions, measured as training-associated changes in transcript expression. Briefly, for 28% and 24% of the transcripts, training was associated with changes in expression in per-total-RNA and per-sample-size scenarios, but not in the per-library-size scenario. At week 2, this led to opposite conclusions for 5% of the transcripts between per-library-size and per-sample-size datasets (↑ vs. ↓, respectively). ConclusionScientists should be explicit with their choice of normalization strategies and should interpret the results of gene expression analyses with caution. This is particularly important for data sets involving a limited number of genes or involving growing or differentiating cellular models, where the risk of biased conclusions is pronounced.

GoldBricks: An Improved Cloning Strategy that Combines Features of Golden Gate and Biobricks for Better Efficiency and Usability

ABSTRACT With increasing complexity of expression studies and the repertoire of characterized sequences, combinatorial cloning has become a common necessity. Techniques like Biobricks and Golden Gate aim to standardize and speed up the process of cloning large constructs while enabling sharing of resources. The Biobricks format provides a simplified and flexible approach to endless assembly with a compact library and useful intermediates but is a slow process, joining only two parts in a cycle. Golden Gate improves upon the speed with use of TypeIIS enzymes and joins several parts in a cycle but requires a larger library of parts and logistical inefficiencies scale up significantly in the multigene format. We present here a method that provides improvement over these techniques by combining their features. By using Type IIS enzymes in a format like Biobricks, we have enabled a faster and efficient assembly with reduced scarring, which performs at a similarly fast pace as Golden Gate, but significantly reduces library size and user input. Additionally, this method enables faster assembly of operon-style constructs, a feature requiring extensive workaround in Golden Gate. Our format allows such inclusions resulting in faster and more efficient assembly.

RUV-III-NB: Normalization of single cell RNA-seq Data

Motivation: Despite numerous methodological advances, the normalization of single cell RNA-seq (scRNA-seq) data remains a challenging task. The performance of different methods can vary greatly across datasets. Part of the reason for this is the different kinds of unwanted variation, including library size, batch and cell cycle effects, and the association of these with the biology embodied in the cells. A normalization method that does not explicitly take into account cell biology risks removing some of the signal of interest. Here we propose RUV-III-NB, a statistical method that can be used to adjust counts for library size and batch effects. The method uses the concept of pseudo-replicates to ensure that relevant features of the unwanted variation are only inferred from cells with the same biology and return adjusted sequencing count as output. Results: Using five publicly available datasets that encompass different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve differential expression analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent across the five datasets and is not sensitive to the number of factors assumed to contribute to the unwanted variation. It also shows promise for removing other kinds of unwanted variation such as platform effects. Availability: The method is implemented as a publicly available R package available from https://github.com/limfuxing/ruvIIInb. Contact: [email protected], [email protected] Supplementary information: Online Supplementary Methods

Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr

Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.

cDNA display coupled with next-generation sequencing for rapid activity-based screening: Comprehensive analysis of transglutaminase substrate preference

AbstractcDNA display is an in vitro display technology based on a covalent linkage between a protein and its corresponding mRNA/cDNA, where a stable complex is formed suitable for a wide range of selection conditions. A great advantage of cDNA display is the ability to handle enormous library size (1012) in a microtube scale, in a matter of days. To harness its benefits, we aimed at developing a platform which combines the advantages of cDNA display with high-throughput and accuracy of next-generation sequencing (NGS) for the selection of preferred substrate peptides of transglutaminase 2 (TG2), a protein cross-linking enzyme. After the optimization of the platform by the repeated screening of binary model libraries consisting of the substrate and non-substrate peptides at different ratios, screening and selection of combinatorial peptide library randomized at positions -1, +1, +2, and +3 from the glutamine residue was carried out. Enriched cDNA complexes were analyzed by NGS and bioinformatics, revealing the comprehensive amino acid preference of the TG2 at targeted positions of the peptide backbone. This is the first report on the cDNA display/NGS screening system to yield comprehensive data on TG substrate preference. Although some issues remain to be solved, this platform can be applied to the selection of other TGs and easily adjusted for the selection of other peptide substrates and even larger biomolecules.