Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing therhizosphere(immediately surroundingthe root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation1-3. Colonization of the root occurs despite a sophisticated plant immune system4,5, suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plantmicrobe interactions derived from complex soil communities.

Immune systems distinguish "self" from "nonself" to maintain homeostasis and must differentially gate access to allow colonization by potentially beneficial, nonpathogenic microbes. Plant roots grow within extremely diverse soil microbial communities but assemble a taxonomically limited root-associated microbiome. We grew isogenic Arabidopsis thaliana mutants with altered immune systems in a wild soil and also in recolonization experiments with a synthetic bacterial community. We established that biosynthesis of, and signaling dependent on, the foliar defense phytohormone salicylic acid is required to assemble a normal root microbiome. Salicylic acid modulates colonization of the root by specific bacterial families. Thus, plant immune signaling drives selection from the available microbial communities to sculpt the root microbiome.

Analysis of single-cell RNA-seq data begins with the pre-processing of reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto and bustools programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses.

We describe a universal sample multiplexing method for single-cell RNA-seq in which cells are chemically labeled with identifying DNA oligonucleotides. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing a cost effective means to survey cell populations from large experiments and clinical samples with the depth and resolution of single-cell RNA-seq.Massively parallelized single-cell RNA-sequencing (scRNA-seq) is transforming our view of complex tissues and yielding new insights into functional states of heterogeneous cell populations. Currently, individual scRNA-seq experiments can routinely probe the transcriptomes of more than ten thousand cells 1,2 , and in the past year the first datasets approaching and exceeding one million cells have been reported 3,4 . However, despite numerous technical breakthroughs that have increased cell capacity of many scRNA-seq platforms, researchers are at present limited in the number of samples that can be assayed.Many biological and therapeutic problems rely on finding genes or signals responsible for a phenotype of interest, but the enormous space of possible variables calls for screening hundreds, or even thousands, of conditions. At present, analyzing genetic, signaling, and drug perturbations (and their combinations) at