Cerebral organoids display dynamic clonal growth and tunable tissue replenishment.

During brain development, neural progenitors expand through symmetric divisions before giving rise to differentiating cell types via asymmetric divisions. Transition between those modes varies among individual neural stem cells, resulting in clones of different sizes. Imaging-based lineage tracing allows for lineage analysis at high cellular resolution but systematic approaches to analyse clonal behaviour of entire tissues are currently lacking. Here we implement whole-tissue lineage tracing by genomic DNA barcoding in 3D human cerebral organoids, to show that individual stem cell clones produce progeny on a vastly variable scale. By using stochastic modelling we find that variable lineage sizes arise because a subpopulation of lineages retains symmetrically dividing cells. We show that lineage sizes can adjust to tissue demands after growth perturbation via chemical ablation or genetic restriction of a subset of cells in chimeric organoids. Our data suggest that adaptive plasticity of stem cell populations ensures robustness of development in human brain organoids.

Neutral competition explains the clonal composition of neural organoids.

Neural organoids model the development of the human brain and are an indispensable tool for studying neurodevelopment. Whole-organoid lineage tracing has revealed the number of progenies arising from each initial stem cell to be highly diverse, with lineage sizes ranging from one to more than 20,000 cells. This high variability exceeds what can be explained by existing stochastic models of corticogenesis and indicates the existence of an additional source of stochasticity. To explain this variability, we introduce the SAN model which distinguishes Symmetrically diving, Asymmetrically dividing, and Non-proliferating cells. In the SAN model, the additional source of stochasticity is the survival time of a lineage's pool of symmetrically dividing cells. These survival times result from neutral competition within the sub-population of all symmetrically dividing cells. We demonstrate that our model explains the experimentally observed variability of lineage sizes and derive the quantitative relationship between survival time and lineage size. We also show that our model implies the existence of a regulatory mechanism which keeps the size of the symmetrically dividing cell population constant. Our results provide quantitative insight into the clonal composition of neural organoids and how it arises. This is relevant for many applications of neural organoids, and similar processes may occur in other developing tissues both in vitro and in vivo.

Genome replication in asynchronously growing microbial populations.

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. In a growing cell culture, genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory linking this pattern to the replication program is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in budding yeast and Escherichia coli. In both cases, our model results are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.

Insertion Pool Sequencing for Insertional Mutant Analysis in Complex Host-Microbe Interactions.

Insertional mutant libraries of microorganisms can be applied in negative depletion screens to decipher gene functions. Because of underrepresentation in colonized tissue, one major bottleneck is analysis of species that colonize hosts. To overcome this, we developed insertion pool sequencing (iPool-Seq). iPool-Seq allows direct analysis of colonized tissue due to high specificity for insertional mutant cassettes. Here, we describe detailed protocols for infection as well as genomic DNA extraction to study the interaction between the corn smut fungus Ustilago maydis and its host maize. In addition, we provide protocols for library preparation and bioinformatic data analysis that are applicable to any host-microbe interaction system. © 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

In vivo insertion pool sequencing identifies virulence factors in a complex fungal-host interaction.

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.

TRUmiCount: correctly counting absolute numbers of molecules using unique molecular identifiers.

Motivation: Counting 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. Results: We 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 implementation: TRUmiCount 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.