Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN) for cross-modal individual analysis of the whole brain.

Cross-modal analysis of the same whole brain is an ideal strategy to uncover brain function and dysfunction. However, it remains challenging due to the slow speed and destructiveness of traditional whole-brain optical imaging techniques. Here we develop a new platform, termed Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN), for non-destructive, high-speed, 3D imaging of ex vivo rodent, ferret, and non-human primate brains. Using an optimally designed image acquisition scheme and an accompanying machine-learning algorithm, PATTERN extracts signals of genetically-encoded probes from photobleaching-based temporal modulation and enables reliable visualization of neural projection in the whole central nervous system with 3D isotropic resolution. Without structural and biological perturbation to the sample, PATTERN can be combined with other whole-brain imaging modalities to acquire the whole-brain image with both high resolution and morphological fidelity. Furthermore, cross-modal transcriptome analysis of an individual brain is achieved by PATTERN imaging. Together, PATTERN provides a compatible and versatile strategy for brain-wide cross-modal analysis at the individual level.

Achieving single-cell-resolution lineage tracing in zebrafish by continuous barcoding mutations during embryogenesis.

Unraveling the lineage relationships of all descendants from a zygote is fundamental to advancing our understanding of developmental and stem cell biology. However, existing cell barcoding technologies in zebrafish lack the resolution to capture the majority of cell divisions during embryogenesis. A recently developed method, a substitution mutation-aided lineage-tracing system (SMALT), successfully reconstructed high-resolution cell phylogenetic trees for Drosophila melanogaster. Here, we implement the SMALT system in zebrafish, recording a median of 14 substitution mutations on a one-kilobase-pair barcoding sequence for one-day post-fertilization embryos. Leveraging this system, we reconstruct four cell lineage trees for zebrafish fin cells, encompassing both original and regenerated fin. Each tree consists of hundreds of internal nodes with a median bootstrap support of 99%. Analysis of the obtained cell lineage trees reveals that regenerated fin cells mainly originate from cells in the same part of the fins. Through multiple times sampling germ cells from the same individual, we show the stability of the germ cell pool and the early separation of germ cell and somatic cell progenitors. Our system offers the potential for reconstructing high-quality cell phylogenies across diverse tissues, providing valuable insights into development and disease in zebrafish.

A statistical method for quantifying progenitor cells reveals incipient cell fate commitments.

Quantifying the number of progenitor cells that found an organ, tissue or cell population is of fundamental importance for understanding the development and homeostasis of a multicellular organism. Previous efforts rely on marker genes that are specifically expressed in progenitors. This strategy is, however, often hindered by the lack of ideal markers. Here we propose a general statistical method to quantify the progenitors of any tissues or cell populations in an organism, even in the absence of progenitor-specific markers, by exploring the cell phylogenetic tree that records the cell division history during development. The method, termed targeting coalescent analysis (TarCA), computes the probability that two randomly sampled cells of a tissue coalesce within the tissue-specific monophyletic clades. The inverse of this probability then serves as a measure of the progenitor number of the tissue. Both mathematic modeling and computer simulations demonstrated the high accuracy of TarCA, which was then validated using real data from nematode, fruit fly and mouse, all with related cell phylogenetic trees. We further showed that TarCA can be used to identify lineage-specific upregulated genes during embryogenesis, revealing incipient cell fate commitments in mouse embryos.

Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development.

Mapping the cell phylogeny of a complex multicellular organism relies on somatic mutations accumulated from zygote to adult. Available cell barcoding methods can record about three mutations per barcode, enabling only low-resolution mapping of the cell phylogeny of complex organisms. Here we developed SMALT, a substitution mutation-aided lineage-tracing system that outperforms the available cell barcoding methods in mapping cell phylogeny. We applied SMALT to Drosophila melanogaster and obtained on average more than 20 mutations on a three-kilobase-pair barcoding sequence in early-adult cells. Using the barcoding mutations, we obtained high-quality cell phylogenetic trees, each comprising several thousand internal nodes with 84-93% median bootstrap support. The obtained cell phylogenies enabled a population genetic analysis that estimates the longitudinal dynamics of the number of actively dividing parental cells (Np) in each organ through development. The Np dynamics revealed the trajectory of cell births and provided insight into the balance of symmetric and asymmetric cell division.

An instantaneous coalescent method insensitive to population structure.

Conventional coalescent inferences of population history make the critical assumption that the population under examination is panmictic. However, most populations are structured. This complicates the prevailing coalescent analyses and sometimes leads to inaccurate estimates. To develop a coalescent method unhampered by population structure, we perform two analyses. First, we demonstrate that the coalescent probability of two randomly sampled alleles from the immediate preceding generation (one generation back) is independent of population structure. Second, motivated by this finding, we propose a new coalescent method: i-coalescent analysis. The i-coalescent analysis computes the instantaneous coalescent rate by using a phylogenetic tree of sampled alleles. Using simulated data, we broadly demonstrate the capability of i-coalescent analysis to accurately reconstruct population size dynamics of highly structured populations, although we find this method often requires larger sample sizes for structured populations than for panmictic populations. Overall, our results indicate i-coalescent analysis to be a useful tool, especially for the inference of population histories with intractable structure such as the developmental history of cell populations in the organs of complex organisms.

Drivers of Mating Type Composition in Tetrahymena thermophila.

Sex offers advantages even in primarily asexual species. Some ciliates appear to utilize such reproductive strategy with many mating types. However, the factors determining the composition of mating types in the unicellular ciliate Tetrahymena thermophila are poorly understood, and this is further complicated by non-Mendelian determination of mating type in the offspring. We therefore developed a novel population genetics model to predict how various factors influence the dynamics of mating type composition, including natural selection. The model predicted either the coexistence of all seven mating types or fixation of a single mating type in a population, depending on parameter combinations, irrespective of natural selection. To understand what factor(s) may be more influential and to test the validity of theoretical prediction, five replicate populations were maintained in laboratory such that several factors could be controlled or measured. Whole-genome sequencing was used to identify newly arising mutations and determine mating type composition. Strikingly, all populations were found to be driven by strong selection on newly arising beneficial mutations to fixation of their carrying mating types, and the trajectories of speed to fixation agreed well with our theoretical predictions. This study illustrates the evolutionary strategies that T. thermophila can utilize to optimize population fitness.

Decoupling gene functions from knockout effects by evolutionary analyses.

Genic functions have long been confounded by pleiotropic mutational effects. To understand such genetic effects, we examine HAP4, a well-studied transcription factor in Saccharomyces cerevisiae that functions by forming a tetramer with HAP2, HAP3 and HAP5. Deletion of HAP4 results in highly pleiotropic gene expression responses, some of which are clustered in related cellular processes (clustered effects) while most are distributed randomly across diverse cellular processes (distributed effects). Strikingly, the distributed effects that account for much of HAP4 pleiotropy tend to be non-heritable in a population, suggesting they have few evolutionary consequences. Indeed, these effects are poorly conserved in closely related yeasts. We further show substantial overlaps of clustered effects, but not distributed effects, among the four genes encoding the HAP2/3/4/5 tetramer. This pattern holds for other biochemically characterized yeast protein complexes or metabolic pathways. Examination of a set of cell morphological traits of the deletion lines yields consistent results. Hence, only some deletion effects of a gene support related biochemical understandings with the rest being often pleiotropic and evolutionarily decoupled from the gene's normal functions. This study suggests a new framework for reverse genetic analysis.