Loss-of-function (LOF) screens provide a powerful approach to identify regulators in biological processes. Pioneered in laboratory animals, LOF screens of human genes are currently restricted to two-dimensional (2D) cell culture hindering testing of gene functions requiring tissue context. Here we present CRISPR-LIneage tracing at Cellular resolution in Heterogenous Tissue (CRISPR-LICHT), enabling parallel LOF studies in human cerebral organoid tissue. We used CRISPR-LICHT to test 173 microcephaly candidate genes revealing 25 to be involved in known and uncharacterized microcephaly-associated pathways. We characterized Immediate Early Response 3 Interacting Protein 1 (IER3IP1) regulating the unfolded protein response (UPR) and extracellular matrix (ECM) protein secretion crucial for tissue integrity, with dysregulation resulting in microcephaly. Our human tissue screening technology identifies microcephaly genes and mechanisms involved in brain size control.

MotivationCounting molecules using next-generation sequencing (NGS) suffers from PCR amplification bias, which reduces the accuracy of many quantitative NGS-based experimental methods such as RNA-Seq. This is true even if molecules are made distinguishable using unique molecular identifiers (UMIs) before PCR amplification, and distinct UMIs are counted instead of reads: Molecules that are lost entirely during the sequencing process will still cause underestimation of the molecule count, and amplification artifacts like PCR chimeras create phantom UMIs and thus cause over-estimation.ResultsWe introduce the TRUmiCount algorithm to correct for both types of errors. The TRUmiCount algorithm is based on a mechanistic model of PCR amplification and sequencing, whose two parameters have an immediate physical interpretation as PCR efficiency and sequencing depth and can be estimated from experimental data without requiring calibration experiments or spike-ins. We show that our model captures the main stochastic properties of amplification and sequencing, and that it allows us to filter out phantom UMIs and to estimate the number of molecules lost during the sequencing process. Finally, we demonstrate that the phantom-filtered and loss-corrected molecule counts computed by TRUmiCount measure the true number of molecules with considerably higher accuracy than the raw number of distinct UMIs, even if most UMIs are sequenced only once as is typical for single-cell RNA-Seq.Availability and implementationTRUmiCount is available at http://www.cibiv.at/software/trumicount and through Bioconda (http://bioconda.github.io).Supplementary information Supplementary information is available at Bioinformatics online.

Large-scale insertional mutagenesis screens can be powerful genome-wide tools if they are streamlined with efficient downstream analysis, which is a serious bottleneck in complex biological systems. A major impediment to the success of next-generation sequencing (NGS)-based screens for virulence factors is that the genetic material of pathogens is often underrepresented within the eukaryotic host, making detection extremely challenging. We therefore established insertion Pool-Sequencing (iPool-Seq) on maize infected with the biotrophic fungus U. maydis. iPool-Seq features tagmentation, unique molecular barcodes, and affinity purification of pathogen insertion mutant DNA from in vivo-infected tissues. In a proof of concept using iPool-Seq, we identified 28 virulence factors, including 23 that were previously uncharacterized, from an initial pool of 195 candidate effector mutants. Because of its sensitivity and quantitative nature, iPool-Seq can be applied to any insertional mutagenesis library and is especially suitable for genetically complex setups like pooled infections of eukaryotic hosts.