Immune cells are particularly instructive subjects for this kind of analysis because their function depends on rapid, spatially organized surface events. A T cell encountering an antigen-presenting cell reorganizes its membrane within minutes, clustering receptors and adaptor proteins into a structured interface known as the immunological synapse. B cells undergo analogous rearrangements when their B cell receptor binds antigen. Natural killer cells survey target cells and form activating or inhibitory contacts whose outcomes depend on the balance of receptor signals at the contact site. In each case, the physical state of the membrane at the interface is not incidental; it determines which proteins can diffuse into or out of the contact zone, which lipid species segregate there, and how efficiently the cytoskeleton can be remodeled to stabilize or dissolve the interaction.

The biophysical basis of membrane order lies in the composition and behavior of the lipid bilayer itself. Sphingolipids and phospholipids with saturated acyl chains pack tightly and adopt extended conformations, producing regions of high order. Unsaturated phospholipids, with kinks introduced by double bonds, disrupt packing and lower local order. Cholesterol intercalates between phospholipids and has a concentration-dependent effect: at moderate levels it rigidifies fluid bilayers and promotes the coalescence of ordered domains, while at high levels it can increase order further in saturated lipid environments. These interactions underlie the long-standing hypothesis of lipid rafts, nanoscale assemblies enriched in sphingolipids, cholesterol, and certain lipid-anchored proteins that have been proposed to serve as platforms for signaling. Direct visualization of rafts in living cells proved technically elusive for decades because the domains are small, transient, and below the diffraction limit of conventional microscopy, which fueled considerable debate about their physiological relevance.

Probe-based imaging has been central to resolving this debate. Environmentally sensitive dyes such as laurdan and its derivatives report on the hydration and packing of their lipid surroundings through shifts in their emission spectra, allowing order to be quantified as a generalized polarization value. When such probes are targeted to specific leaflets of the plasma membrane or conjugated to molecules that partition preferentially into ordered or disordered phases, they provide spatially resolved readouts of membrane physics in live cells. The interpretation of these measurements requires care, because probe partitioning can itself perturb the membrane, and spectral readouts can be confounded by factors such as pH, probe concentration, and photobleaching. Advances in probe chemistry, calibration standards, and imaging modalities have progressively addressed these concerns, making it possible to compare order measurements across cell types and experimental conditions with increasing confidence.

Super-resolution microscopy techniques have further transformed the field by bringing the relevant length scales within reach. Stimulated emission depletion microscopy, photoactivated localization microscopy, and stochastic optical reconstruction microscopy each achieve effective resolutions well below the diffraction limit, revealing that proteins and lipids once thought to be uniformly distributed actually occupy discrete nanoscale clusters. Combining these structural methods with spectral imaging of order-sensitive probes allows researchers to ask whether regions of high membrane order coincide with clusters of signaling proteins, and whether such coincidence changes upon receptor activation. In immune cells, this combination has shown that ordered domains accumulate at sites of receptor engagement and that disrupting ordered lipid phases, for example by depleting cholesterol or inhibiting sphingolipid synthesis, impairs signaling outputs such as calcium flux, phosphorylation cascades, and cytokine production.

The relationship between membrane order and the actin cytoskeleton adds another layer of regulatory complexity. Cortical actin filaments exert forces on the overlying membrane, creating regions of tension and constriction that can influence lipid phase behavior. Actin-driven structures such as membrane ruffles, microvilli, and picket-and-fence arrangements compartmentalize lateral diffusion, effectively corralling proteins and lipids into transient domains. Conversely, the lipid composition of the membrane affects how actin-binding proteins attach to the cytoplasmic face, creating a bidirectional feedback loop. In migrating immune cells, leading-edge membranes enriched in unsaturated lipids and low order support the protrusive activity needed for chemotaxis, while the uropod exhibits different physical properties that promote adhesion and retraction. Mapping order across a polarized cell therefore reveals how physical heterogeneity aligns with functional polarity.

Pathogens have evolved to exploit membrane physical properties during infection, which underscores the selective pressures shaping these systems. Enveloped viruses bud from membranes whose lipid composition facilitates assembly and release, and some viruses preferentially incorporate ordered lipid domains into their envelopes. Bacterial toxins that bind cholesterol or sphingomyelin use ordered domains as points of attachment for pore formation. Intracellular pathogens manipulate host membrane traffic and lipid metabolism to create replication niches with altered physical properties. In each scenario, the immune response must contend with a membrane environment that the pathogen has actively reshaped, and measurements of membrane order in infected cells can reveal these manipulations as measurable shifts in surface biophysics.

Aging and metabolic state also leave imprints on membrane order. Dietary lipid composition influences the saturation profile of membrane phospholipids over time, and age-associated changes in lipid metabolism have been documented in immune cells from multiple organisms. Membranes from aged T cells, for example, show altered cholesterol content and modified order characteristics that correlate with diminished signaling capacity upon antigen stimulation. Metabolic diseases such as obesity and diabetes, which alter circulating lipid profiles, produce measurable changes in the membrane properties of circulating leukocytes. These observations suggest that membrane order could serve as an integrative readout of an organism’s metabolic and inflammatory history, encoded in the physical state of its immune cell surfaces.

Therapeutically, the sensitivity of membrane order to lipid metabolism opens avenues for intervention. Statins, which reduce cholesterol synthesis, have immunomodulatory effects that may partly reflect changes in membrane organization. Drugs targeting sphingolipid metabolism, such as inhibitors of sphingomyelin synthase or glucosylceramide synthase, alter ordered domain abundance and have shown effects on inflammatory signaling. Fingolimod, a sphingosine-1-phosphate receptor modulator used in multiple sclerosis, acts in part through receptor internalization but also engages with the broader biology of sphingolipid-enriched membranes. Understanding how such agents redistribute membrane order across immune cell subsets could explain some of their off-target effects and guide the design of compounds that tune immune responses through membrane biophysics rather than direct receptor antagonism.

Methodological standardization remains an important challenge for the field. Different probes report on different aspects of membrane physics, and values obtained with one dye are not directly comparable to those from another without careful cross-calibration. Sample preparation, temperature, imaging parameters, and analysis pipelines all influence measured values, and the field has not yet converged on universally accepted reference standards. Efforts to establish standardized protocols, share calibration reagents, and report measurements in ways that facilitate comparison across laboratories will be essential if membrane order is to mature from a research measurement into a reproducible biomarker. The application of machine learning approaches to extract order-related features from large imaging datasets may also accelerate progress by identifying patterns that manual analysis would miss.

The diversity of immune cell subsets presents both an opportunity and a complication. Myeloid cells, lymphocytes, and innate lymphoid cells each maintain distinct lipidomes shaped by their developmental programs and functional demands. Within a single subset, activation state, differentiation stage, and tissue microenvironment further modify membrane composition. A dendritic cell maturing in response to pathogen-associated molecular patterns remodels its membrane as part of its transition from antigen capture to antigen presentation. Tissue-resident macrophages adapt their membrane properties to the lipid milieu of their organ of residence, which differs substantially between brain, lung, liver, and adipose tissue. Comprehensive maps of membrane order across this diversity would require systematic sampling, but the resulting atlas could reveal how physical membrane states encode functional specialization in ways that transcriptomic or proteomic measurements alone do not capture.

Looking forward, the integration of membrane order measurements with other single-cell modalities promises a more complete picture of immune regulation. Combining order imaging with live-cell reporters of signaling activity, such as fluorescent biosensors for kinase activity or calcium, would allow direct testing of causal relationships between membrane physics and signal transduction at the single-cell level. Pairing order measurements with lipidomics would connect physical readouts to their molecular determinants. Spatial transcriptomics and proteomics of tissue sections could place membrane biophysical states in their anatomical and pathological contexts. As these datasets accumulate, the plasma membrane’s physical organization may come to be recognized as a fundamental layer of cellular regulation, one that immune cells exploit with particular sophistication and one that offers distinct targets for therapeutic modulation of immunity.

Subject of Research: Plasma membrane order maps functional diversity in immune cells

Article Title: Plasma membrane order maps functional diversity in immune cells

Article References: Andronico, L. A., Gurdap, C. O., Arora, A., Ragaller, F., Sandoz, P. A., Jiang, Y., Giatrellis, S., de Boer, L. L., Carannante, V., Iskrak, S., Mikes, J., Buggert, M., Österborg, A., Önfelt, B., Klymchenko, A. S., Brodin, P., & Sezgin, E. (2026). Plasma membrane order maps functional diversity in immune cells. Nature Chemical Biology. https://doi.org/10.1038/s41589-026-02322-x

Image Credits: AI Generated

DOI: 10.1038/s41589-026-02322-x

Keywords: Plasma, membrane, order, maps, functional, diversity, immune, cells, scientific research

The study, published in the journal Ocean Microbiology, drew on the metaPR2 database, a curated collection of processed 18S rRNA gene metabarcodes from more than forty studies spanning thousands of samples worldwide. The researchers classified roughly 47,000 marine protist amplicon sequence variants, or ASVs, into functional categories using the Mixoplankton Database, a resource that catalogues known mixotrophic species and their trophic strategies. Four mixoplankton types emerged as the focus of the analysis: constitutive mixoplankton, which build their own chloroplasts; generalist non-constitutive mixoplankton, which steal chloroplasts from a broad range of prey; plastidic specialist non-constitutive mixoplankton, which selectively retain plastids and even nuclear material from specific prey for weeks or months; and endosymbiotic specialist non-constitutive mixoplankton, which harbor long-term photosynthetic symbionts inside their cells.

These distinctions matter because each strategy carries different physiological costs and ecological consequences. Constitutive mixoplankton, which include familiar dinoflagellate and haptophyte genera such as Alexandrium, Karlodinium, and Karenia, can photosynthesize continuously while opportunistically consuming prey. Generalists of the non-constitutive kind, such as ciliates in the genera Strombidium and Laboea, must feed frequently, on scales of hours to days, because their stolen plastids degrade quickly. Plastidic specialists such as Mesodinium and Dinophysis can maintain sequestered photosynthetic machinery for extended periods, while endosymbiotic specialists like Ornithocercus and the green form of Noctiluca scintillans sustain stable partnerships with algal cells that contribute sugars and recycle nutrients derived from prey digestion.

After filtering the data to euphotic-zone samples, those from the upper 200 meters where light penetrates, the final dataset comprised nearly 44,000 ASVs and more than 366 million reads from 4,190 samples reaching latitudes from roughly 74 degrees south to 89 degrees north. Mixoplankton appeared in 94 percent of the samples, confirming their global ubiquity. Overall, mixoplankton accounted for about 7 percent of protist ASVs, corresponding to 3,537 sequence variants and 192 species, which represents some 44 percent of the species listed in the Mixoplankton Database. Protozooplankton and parasites dominated the ASV counts at 38 and 24 percent respectively, while diatoms made up 8 percent and other phytoplankton 22 percent.

To untangle the patterns hidden within this enormous dataset, the team turned to self-organizing maps, an unsupervised machine learning technique that condenses thousands of ASV abundance profiles into two-dimensional neuronal grids that can then be hierarchically clustered. Because sequencing methodology, particularly the choice between the V4 and V9 hypervariable regions of the 18S rRNA gene and the seawater filtration strategy, strongly shapes recovered community composition, the researchers deliberately analyzed separate subdatasets defined by consistent methods rather than pooling everything together. The clustering, applied independently to three subdatasets, each explained between 75 and 78 percent of total variance and produced community groupings that aligned with four major oceanic biomes: polar, subpolar to temperate, temperate to subtropical, and subtropical to tropical.

Those biome assignments were far from arbitrary. Principal component ordination and temperature-salinity-nitrate diagrams showed that the machine learning clusters ordered themselves consistently along environmental gradients of temperature, salinity, and nitrate concentration, with statistical tests confirming significant differences among clusters. Polar and subpolar communities were associated with the coldest waters and highest nitrate levels, while temperate, subtropical, and tropical communities corresponded with warmer, nutrient-poor conditions. Salinity played a comparatively weaker structural role, likely because it varies over a relatively narrow range in marine waters. The resulting biogeography matched classical oceanographic regions described in earlier plankton studies, lending confidence to the approach.

Within this framework, each mixoplankton functional type revealed its own ecological signature. Constitutive mixoplankton were broadly distributed across all biomes and showed distributional patterns closely paralleling those of non-diatom phytoplankton, suggesting either functional overlap or shared resource use between the two groups. Generalized additive models, which can capture non-linear relationships, showed that constitutive mixoplankton reached high relative abundances across a wide temperature span from near zero to 30 degrees Celsius, typical oceanic salinities, and low nitrate concentrations, consistent with the idea that mixotrophy confers a competitive advantage when dissolved nutrients are scarce. Endosymbiotic specialists, by contrast, were restricted to warmer subpolar through tropical waters and were largely absent from polar regions, with more than 88 percent of their sequence variants in one subdataset belonging to Collodaria, radiolarian colonies characteristic of oligotrophic open oceans.

The remaining two mixotypes were scarcer but ecologically revealing. Generalist non-constitutive mixoplankton, the least abundant group, consistently co-occurred with diatoms and extended into nitrate-rich regimes of 20 to 30 micromolar, echoing their dependence on frequent ingestion of phototrophic prey that flourish in productive waters; they were also detected in upwelling zones such as the equatorial Pacific and the Agulhas Current. Plastidic specialists spanned all biomes but were sparse, and in this study appeared in lower-nutrient conditions than previously reported, a shift the authors attribute to seasonal sampling differences and the capacity of retained plastids to sustain photosynthesis across varying nutrient regimes. Diatoms themselves, the only protists confidently confirmed as strictly phototrophic, were predominantly tied to cold, nitrate-rich waters, while protozooplankton and parasites displayed trends generally inverse to those of the mixoplankton, hinting at partitioned consumer niches and host-driven distributions.

The analysis also exposed how profoundly methodological choices shape what scientists see. Mixoplankton richness and relative abundance were, respectively, threefold and sixfold higher in the V9 dataset than in the V4 dataset, largely because the Tara Oceans V9 data captured radiolarians whose exceptionally high rRNA gene copy numbers are differentially amplified by the two marker regions. Comparisons of samples sequenced with both markers showed roughly 60 percent species overlap and significantly correlated abundances, yet one endosymbiotic radiolarian, Collozoum amoeboides, appeared three orders of magnitude more abundant in V9 than in V4. Filtration strategy added further complications, since fragile cells can be disrupted during size fractionation while unfractionated samples can mask rarer groups. The authors stress that these discrepancies do not undermine the conclusions but underscore the need for careful, method-aware interpretation.

By placing mixoplankton within the full context of marine protistan communities at a global scale, the study delivers the first community-level assessment of mixoplankton biogeography relative to co-occurring functional types, and it establishes an empirical foundation for incorporating these organisms into predictive models of marine ecosystem dynamics. The researchers argue that future work should prioritize targeted detection of the underrepresented generalist and plastidic specialist types, whose sparse detection partly reflects their small numbers of known species and the fragility of their cells, and should embrace emerging transcriptomic machine learning methods that can infer trophic mode from gene expression in field communities. As oceans warm and nutrient cycles shift, knowing which mixoplankton strategies dominate where, and under what environmental conditions, may prove essential for forecasting how marine food webs and biogeochemical cycles will respond.

The recognition of mixoplankton as a distinct ecological category represents a relatively recent shift in plankton science. For much of the twentieth century, marine protists were sorted into a simple dichotomy of phytoplankton and zooplankton, an arrangement that implicitly assumed photosynthesis and phagotrophy were mutually exclusive trophic modes. Observations of planktonic ciliates carrying algal plastids and dinoflagellates consuming prey date back more than a century, but only with the development of trait-based frameworks and curated databases has the full diversity of these strategies become systematically catalogued.

The ecological stakes of this reclassification are considerable. Because mixoplankton can acquire nutrients through both dissolved uptake and prey ingestion, they occupy a flexible position in microbial food webs, capable of acting as primary producers when inorganic nutrients are scarce and as grazers when prey are abundant. This dual capacity influences how carbon and nitrogen move through planktonic communities, and models that omit mixotrophy risk misallocating energy flow and nutrient recycling pathways.

The global niche patterns documented in the study also carry implications for a changing ocean. As surface waters warm and stratification intensifies, nutrient supply to the euphotic zone is expected to decline in many regions, conditions that favor organisms able to supplement photosynthesis with feeding. The observed affinity of constitutive mixoplankton for warm, oligotrophic waters, and of endosymbiotic specialists for tropical and subtropical biomes, suggests that these groups may expand as such conditions become more widespread, potentially reshaping community composition and the efficiency of biological carbon export.

Equally important is the methodological legacy of the work. By demonstrating that marker gene choice and sample processing measurably alter perceived mixoplankton abundance, the analysis provides a cautionary benchmark for future metabarcoding surveys and underscores the value of standardized, method-aware databases for tracking marine biodiversity over time.

Subject of Research: Environmental structuring of mixoplankton functional types within marine protist communities: a global analysis

Article Title: Environmental structuring of mixoplankton functional types within marine protist communities: a global analysis

Article References: Larsson, M. E., Leles, S. G., Mitra, A., Faure, E., Vaulot, D., & Santoferrera, L. (2026). Environmental structuring of mixoplankton functional types within marine protist communities: a global analysis. Ocean Microbiology, 2(1), Article 1. https://doi.org/10.1186/s44375-026-00007-3

Image Credits: AI Generated

DOI: 10.1186/s44375-026-00007-3

Keywords: Environmental, structuring, mixoplankton, functional, types, within, marine, protist, communities, global, analysis, scientific research