Biology subjects

Reyes, M.

Publications and source records attributed to Reyes, M..

4 recordsLinked to original sources

bioRxiv2018

Resource supply drives even spacing of individuals along multiple trait axes in light-limited phytoplankton populations

Individual-level variation arising from responses to environmental gradients influences population and community dynamics. How such responses empirically relate to the mechanisms that govern species coexistence is however not well understood. Previous results from lake phytoplankton communities suggested that the evenness of organisms in multidimensional trait space increases with resource limitation, possibly due to resource partitioning at the individual level. Here we experimentally tested the emergence of this pattern by growing two phytoplankton species (Pseudokirchneriella subcapitata, Microcystis aeruginosa) under a gradient of light intensity, in monoculture and jointly. Under low light (resource) conditions, the populations diversified into a wide range of phenotypes, which were evenly distributed in multidimensional trait space (defined by four pigment-related trait dimensions), confirming the observed field pattern. Our results provide prime experimental evidence that resource limitation induces even spacing of conspecific and heterospecific microbial phenotypes along trait axes, and advances our understanding of trait-based coexistence.

ecology↗

bioRxiv2018

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

Scanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

ecology↗

bioRxiv2018

Multiplexed enrichment and genomic profiling of peripheral immune cell subsets on a microfluidic chip

The human immune system consists of many specialized cell subsets that simultaneously carry out a diverse range of functions using overlapping pathways and signals. Subset-specific immune profiling can resolve immune activity in autoimmune disease, cancer immunity, and infectious disease that may not be discoverable or detectable in analyses of crude blood samples. The activity of specific subsets may help predict the course of disease and response to therapy in certain patient populations. Here, we present a low-input microfluidic system for sorting immune cells into subsets and profiling their cellular states by gene expression analysis using full-length RNA-seq. Our system is robust and has the potential to make multiplexed subset-specific analysis routine in many research laboratories and clinical settings. We validate the devices technical performance by benchmarking its subset enrichment and genomic profiling performance against standard protocols. We make the added value of subset-resolved profiling over crude samples clear through ex vivo experiments that show subset-specific stimulated responses. Finally, we demonstrate the scalability of our device by profiling four immune cell subsets in blood from systemic lupus erythematosus (SLE) patients and matched controls enrolled in a clinical study. The results from our initial cohort confirm the role of type I interferons in lupus pathogenesis and further show that the canonical interferon signature for SLE is prominent in B cells, demonstrating the ability of our integrated analytical platform to identify cell-specific disease signatures.

bioengineering↗

bioRxiv2017

The predictability of a lake phytoplankton community, from hours to years

Forecasting anthropogenic changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time scales. Communities were highly predictable over hours to months: model R2 decreased from 0. 89 at 4 hours to 0.75 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell density were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can help elucidate the mechanisms underlying ecological dynamics and set prediction targets for process-based models.

ecology↗