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DOE OSTI2021

Disease modeling for Mucopolysaccharidosis type IIIB using patient derived induced pluripotent stem cells

Highlights: • Establishment of a patient-specific neuronal model for Mucopolysaccharidosis type IIIB using iPSCs. • Lysosomes enlarged and secondary lipid accumulated in Mucopolysaccharidosis type IIIB iPSC-derived neuronal cells. • Recombinant NAGLU, δ-tocopherol, and HPBCD reduced the disease phenotypes in MPS IIIB iPSC-derived neuronal cells. Mucopolysaccharidosis type IIIB (MPS IIIB) is a lysosomal disease caused by mutations in the NAGLU gene encoding α-N-acetylglucosaminidase (NAGLU) which degrades heparan sulfate in lysosomes. Deficiency in NAGLU results in lysosomal accumulation of glycosaminoglycans (GAGs) and neurological symptoms. Currently, there is no effective treatment or cure for this disease. In this study, induced pluripotent stem cell lines were established from two MPS IIIB patient fibroblast lines and differentiated into neural stem cells and neurons. MPS IIIB neural stem cells exhibited NAGLU deficiency accompanied with GAG accumulation, as well as lysosomal enlargement and secondary lipid accumulation. Treatments with recombinant NAGLU, δ-tocopherol, and 2-hydroxypropyl-b-cyclodextrin significantly reduced the disease phenotypes in these cells. These results indicate the MPS IIIB neural stem cells and neurons have the disease relevant phenotype and can be used as a cell-based disease model system for evaluation of drug efficacy and compound screening for drug development.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Characteristics of macrophages from myelodysplastic syndrome microenvironment

Highlights: • MDS macrophages exhibited more M2-related characteristics. • Ferric chloride polarized MDS macrophages to exhibit more M1-related characteristics. • DFO partially reversed the M1-related characteristics induced by ferric chloride. Myelodysplastic syndrome (MDS) is a heterogeneous group of clonal hematopoietic neoplasms. The progression of malignancy is closely associated with immune regulation. Macrophages are indispensable tissue components and have been proposed to play a role in the pathophysiology of hematopoietic malignancies. However, the specific role of macrophages in the development of MDS remains unclear. Here, we investigated the characteristics and phenotypic evolution of macrophages from patients with MDS. Macrophages from patients with MDS expressed CD68, CD86 and CD163. Furthermore, MDS macrophages exhibited more M2-related characteristics. Moreover, a number of phenotype-associated genes in MDS macrophages exhibited diverse responses to iron overload or iron chelation upon stimulation by ferric chloride or deferoxamine (DFO, an iron chelator). Ferric chloride polarized MDS macrophages to exhibit more M1-related characteristics, a phenomenon that could be partially reversed by DFO. Therefore, this study reveals the characteristics and phenotypic evolution of MDS macrophages and broadens the knowledge of macrophage plasticity in hematopoietic malignancies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Root system architecture in cereals: progress, challenges and perspective

We report roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this ‘hidden half’ provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A rapid assay for assessing bacterial effects on Arabidopsis thermotolerance

Abstract Background The role of beneficial microbes in mitigating plant abiotic stress has received considerable attention. However, the lack of a reproducible and relatively high-throughput screen for microbial contributions to plant thermotolerance has greatly limited progress in this area, this slows the discovery of novel beneficial isolates and the processes by which they operate. Results We designed a rapid phenotyping method to assess the effects of bacteria on plant host thermotolerance. After testing multiple growth conditions, a hydroponic system was selected and used to optimize an Arabidopsis heat shock regime and phenotypic evaluation. Arabidopsis seedlings germinated on a PTFE mesh disc were floated onto a 6-well plate containing liquid MS media, then subjected to heat shock at 45 °C for various duration. To characterize phenotype, plants were harvested after four days of recovery to measure chlorophyll content. The method was extended to include bacterial isolates and to quantify bacterial contributions to host plant thermotolerance. As an exemplar, the method was used to screen 25 strains of the plant growth promoting Variovorax spp. for enhanced plant thermotolerance. A follow-up study demonstrated the reproducibility of this assay and led to the discovery of a novel beneficial interaction. Conclusions This method enables rapid screening of individual bacterial strains for beneficial effects on host plant thermotolerance. The throughput and reproducibility of the system is ideal for testing many genetic variants of Arabidopsis and bacterial strains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Accurate determination of genotypic variance of cell wall characteristics of a Populus trichocarpa pedigree using high-throughput pyrolysis-molecular beam mass spectrometry

Abstract Background Pyrolysis-molecular beam mass spectrometry (py-MBMS) analysis of a pedigree of Populus trichocarpa was performed to study the phenotypic plasticity and heritability of lignin content and lignin monomer composition. Instrumental and microspatial environmental variability were observed in the spectral features and corrected to reveal underlying genetic variance of biomass composition. Results Lignin-derived ions (including m/z 124, 154, 168, 194, 210 and others) were highly impacted by microspatial environmental variation which demonstrates phenotypic plasticity of lignin composition in Populus trichocarpa biomass. Broad-sense heritability of lignin composition after correcting for microspatial and instrumental variation was determined to be H 2 = 0.56 based on py-MBMS ions known to derive from lignin. Heritability of lignin monomeric syringyl/guaiacyl ratio ( S / G ) was H 2 = 0.81. Broad-sense heritability was also high (up to H 2 = 0.79) for ions derived from other components of the biomass including phenolics (e.g., salicylates) and C5 sugars (e.g., xylose). Lignin and phenolic ion abundances were primarily driven by maternal effects, and paternal effects were either similar or stronger for the most heritable carbohydrate-derived ions. Conclusions We have shown that many biopolymer-derived ions from py-MBMS show substantial phenotypic plasticity in response to microenvironmental variation in plantations. Nevertheless, broad-sense heritability for biomass composition can be quite high after correcting for spatial environmental variation. This work outlines the importance in accounting for instrumental and microspatial environmental variation in biomass composition data for applications in heritability measurements and genomic selection for breeding poplar for renewable fuels and materials.

09 BIOMASS FUELS↗

DOE OSTI2026

APPL Hyperspectral_Imaging_Dataset_for_Heritability_Analysis_in_Populus_trichocarpa

This dataset contains hyperspectral imaging data collected at the Advanced Plant Phenotyping Laboratory (APPL) at Oak Ridge National Laboratory. Natural variants of Populus trichocarpa were imaged using a high-throughput hyperspectral phenotyping pipeline to quantify spectral reflectance traits for downstream quantitative genetics analyses. The dataset includes hyperspectral image files and derived reflectance data products suitable for extracting spectral features across the measured wavelength range (e.g., VNIR and/or SWIR, depending on instrument configuration), along with associated sample metadata (e.g., genotype identifiers, experimental design factors, and imaging run identifiers). These data were generated to support analyses of broad-sense heritability of hyperspectral traits and their relationships with biochemical phenotypes (including lignin traits from Py-MBMS).

APPL↗

DOE OSTI2024

Longitudinal genome-wide association study reveals early QTL that predict biomass accumulation under cold stress in sorghum

Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Arabidopsis thaliana sku mutant seedlings show exaggerated surface-dependent alteration in root growth vector

Roots of wild-type Arabidopsis thaliana seedlings in the Wassilewskija (WS) and Landsberg erecta (Ler) ecotypes often grow aslant on vertical agar surfaces. Slanted root growth always occurs to the right of the gravity vector when the root is viewed through the agar surface, and is not observed in the Columbia ecotype. Right-slanted root growth is surface-dependent and does not result directly from directional environmental stimuli or gradients in the plane of skewing. We have isolated two partially dominant mutations in WS (sku1 and sku2) that show an exaggerated right-slanting root-growth phenotype on agar surfaces. The right-slanting root-growth phenotype of wild-type and mutant roots is not the result of diagravitropism or of an alteration in root gravitropism. It is accompanied by a left-handed rotation of the root about its axis within the elongation zone, the rate of which positively correlates with the degree of right-slanted curvature. Our data suggest that the right-slanting root growth phenotype results from an endogenous structural asymmetry that expresses itself by a directional root-tip rotation.

NASA Program Space Biology↗

NASA NTRSDate not supplied

Mining the Gravity Mutants of Arabidopsis

Gravity mutants are a valuable resource for understanding gravity perception, signaling, and response in plants. A review of 190 publications resulted in a list of 97 loci with mutations that caused a gravitropic phenotype. While all these mutants show some form of gravity phenotype, several are also generally defective in growth. After removing these nonspecific mutants, 76 loci were deemed to have true gravity mutants. The gene list was then used to create a sortable database containing key factors of gravity signaling as well as data on methodologies and the development of mutant lines. Indexing this database has allowed us to pull out trends that were not visible in individual publications. For example, mutants that are unable to rearrange starch statoliths make up a larger proportion of the described inflorescence stems mutants than any other organ. Comparing these statolith mutants across organs shows that root and inflorescence stems consistently display different phenotypes for the same class of defect. The more severe gravity defects are most commonly described in root tissue while other tissues are more likely to show only a delayed or reduced response to gravity. The experience of members of the GeneLab plant Analytics Working Group (AWG) in data visualization and gene mapping have sparked new ideas for utilizing this database. Knowledge of shared traits, mutant development, and growth can provide a new resource for the production of seed lines specialized for their response to gravity.

gravitropism↗

DOE OSTI2020

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. Here, this integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Oleaginous Yeast Biology Elucidated With Comparative Transcriptomics

ABSTRACT Extremophilic yeasts have favorable metabolic and tolerance traits for biomanufacturing‐ like lipid biosynthesis, flavinogenesis, and halotolerance – yet the connection between these favorable phenotypes and strain genotype is not well understood. To this end, this study compares the phenotypes and gene expression patterns of biotechnologically relevant yeasts Yarrowia lipolytica , Debaryomyces hansenii , and Debaryomyces subglobosus grown under nitrogen starvation, iron starvation, and salt stress. To analyze the large data set across species and conditions, two approaches were used: a “network‐first” approach where a generalized metabolic network serves as a scaffold for mapping genes and a “cluster‐first” approach where unsupervised machine learning co‐expression analysis clusters genes. Both approaches provide insight into strain behavior. The network‐first approach corroborates that Yarrowia upregulates lipid biosynthesis during nitrogen starvation and provides new evidence that riboflavin overproduction in Debaryomyces yeasts is overflow metabolism that is routed to flavin cofactor production under salt stress. The cluster‐first approach does not rely on annotation; therefore, the coexpression analysis can identify known and novel genes involved in stress responses, mainly transcription factors and transporters. Therefore, this work links the genotype to the phenotype of biotechnologically relevant yeasts and demonstrates the utility of complementary computational approaches to gain insight from transcriptomics data across species and conditions.

Weintraub, Sarah J. [Department of Bioinformatics ↗

DOE OSTI2021

The Arabidopsis thaliana nucleotide sugar transporter GONST2 is a functional homolog of GONST1

Abstract Glycosylinositolphosphorylceramides (GIPCs) are the predominant lipid in the outer leaflet of the plasma membrane. Characterized GIPC glycosylation mutants have severe or lethal plant phenotypes. However, the function of the glycosylation is unclear. Previously, we characterized Arabidopsis thaliana GONST1 and showed that it was a nucleotide sugar transporter which provides GDP‐mannose for GIPC glycosylation. gonst1 has a severe growth phenotype, as well as a constitutive defense response. Here, we characterize a mutant in GONST1’s closest homolog, GONST2. The gonst2‐ 1 allele has a minor change to GIPC headgroup glycosylation. Like other reported GIPC glycosylation mutants, gonst1‐1gonst2‐1 has reduced cellulose, a cell wall polymer that is synthesized at the plasma membrane. The gonst2‐1 allele has increased resistance to a biotrophic pathogen Golovinomyces orontii but not the necrotrophic pathogen Botrytis cinerea . Expression of GONST2 under the GONST1 promoter can rescue the gonst1 phenotype, indicating that GONST2 has a similar function to GONST1 in providing GDP‐D‐Man for GIPC mannosylation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

The Arabidopsis T‐DNA mutant SALK_008491 carries a 14‐kb deletion on chromosome 3 that provides rare insights into the plant response to dynamic light stress

Abstract In nature, plants experience rapid changes in light intensity and quality throughout the day. To maximize growth, they have established molecular mechanisms to optimize photosynthetic output while protecting components of the light‐dependent reaction and CO 2 fixation pathways. Plant phenotyping of mutant collections has become a powerful tool to unveil the genetic loci involved in environmental acclimation. Here, we describe the phenotyping of the transfer‐DNA (T‐DNA) insertion mutant line SALK_008491, previously known as nhd1‐1 . Growth in a fluctuating light regime caused a loss in growth rate accompanied by a spike in photosystem (PS) II damage and increased non‐photochemical quenching (NPQ). Interestingly, an independent nhd1 null allele did not recapitulate the NPQ phenotype. Through bulk sequencing of a backcrossed segregating F 2 pool, we identified an ~14‐kb large deletion on chromosome 3 (Chr3) in SALK_008491 affecting five genes upstream of NHD1 . Besides NHD1 , which encodes for a putative plastid Na + /H + antiporter, the stromal NAD‐dependent D‐3‐phosphoglycerate dehydrogenase 3 ( PGDH3 ) locus was eradicated. Although some changes in the SALK_008491 mutant's photosynthesis can be assigned to the loss of PGDH3, our follow‐up studies employing respective single mutants and complementation with overlapping transformation‐competent artificial chromosome (TAC) vectors reveal that the exacerbated fluctuating light sensitivity in SALK_008491 mutants result from the simultaneous loss of PGDH3 and NHD1. Altogether, the data obtained from this large deletion‐carrying mutant provide new and unintuitive insights into the molecular mechanisms that function to protect the photosynthetic machinery. Moreover, our study renews calls for caution when setting up reverse genetic studies using T‐DNA lines. Although second‐site insertions, indels, and SNPs have been reported before, large deletion surrounding the insertion site causes yet another problem. Nevertheless, as shown through this research, such unpredictable genetic events following T‐DNA mutagenesis can provide unintuitive insights that allow for understanding complex phenomena such as the plant acclimation to dynamic high light stress.

Lopez, Laura S.↗

DOE OSTI2021

Picturing the future of food

Abstract High‐throughput phenotyping (HTP) has emerged as one of the most exciting and rapidly evolving spaces within plant science. The successful application of phenotyping technologies will facilitate increases in agricultural productivity. High‐throughput phenotyping research is interdisciplinary and may involve biologists, engineers, mathematicians, physicists, and computer scientists. Here we describe the need for additional interest in HTP and offer a primer for those looking to engage with the HTP community. This is a high‐level overview of HTP technologies and analysis methodologies, which highlights recent progress in applying HTP to foundational research, identification of biotic and abiotic stress, breeding and crop improvement, and commercial and production processes. We also point to the opportunities and challenges associated with incorporating HTP across food production to sustainably meet the current and future global food supply requirements.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Subsampling and DNA pooling can increase gains through genomic selection in switchgrass

Genomic selection (GS) can accelerate breeding cycles in perennial crops such as the bioenergy grass switchgrass (Panicum virgatum L.). The sequencing costs of GS can be reduced by pooling DNA samples in the training population (TP), only sequencing TP phenotypic outliers, or pooling candidate population (CP) samples. These strategies were simulated for two traits (spring vigor and anthesis date) in three breeding populations. Sequencing only the outlier 50% of the TP phenotype distribution resulted in a penalty of <5% of the predictive ability, measured using cross-validation. Predictive ability also decreased when sequencing progressively fewer TP DNA pools, but TPs constructed from only two phenotypically contrasting DNA samples retained a mean of >80% predictive ability relative to individual TP sequencing. Novel group testing methods allowed greater than one CP individual to be screened per sequenced DNA sample but resulted in a predictive ability penalty. To determine the impact of reduced sequencing, genetic gain was calculated for seven GS scenarios with variable sequencing budgets. Reduced TP sequencing and most CP pooling methods were superior to individual sequence-based GS when sequencing resources were restricted (2,000 DNA samples per 5-yr cycle). Only one scenario was superior to individual sequencing when sequencing budgets were large (8,000 DNA samples per 5-yr cycle). This study highlights multiple routes for reduced sequencing costs in GS.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Machine learning models for segmentation and classification of cyanobacterial cells

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Huffine, Clair A.↗