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Gravity-driven sinking of “marine snow” sequesters carbon in the ocean, constituting a key biological pump that regulates Earth’s climate. A mechanistic understanding of this phenomenon is obscured by the biological richness of these aggregates and a lack of direct observation of their sedimentation physics. Utilizing a scale-free vertical tracking microscopy in a field setting, we present microhydrodynamic measurements of freshly collected marine snow aggregates from sediment traps. Our observations reveal hitherto-unknown comet-like morphology arising from fluid-structure interactions of transparent exopolymer halos around sinking aggregates. These invisible comet tails slow down individual particles, greatly increasing their residence time. Based on these findings, we constructed a reduced-order model for the Stokesian sedimentation of these mucus-embedded two-phase particles, paving the way toward a predictive understanding of marine snow. 

Tuberculosis (TB) is the world’s deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organismMycobacterium tuberculosis(Mtb) can take nearly 40 d to culture, a required step to determine the pathogen’s antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG. 

Sinking or sedimentation of biological aggregates plays a critical role in carbon sequestration in the ocean and in vertical material fluxes in wastewater treatment plants. In both these contexts, the sinking aggregates are ‘active’, since they are biological hot-spots and are densely colonized by microorganisms including bacteria and sessile protists, some of which generate feeding currents. However, the effect of these feeding currents on the sinking rates, trajectories and mass transfer to these ‘active sinking particles’ has not previously been studied. Here, we use a novel scale-free vertical tracking microscope (a.k.a. gravity machine; Krishnamurthy et al. 2020 Nat. Methods 17 , 1040–1051 ( doi:10.1038/s41592-020-0924-7 )) to follow model sinking aggregates (agar spheres) with attached protists ( Vorticella convallaria ), sinking over long distances while simultaneously measuring local flows. We find that activity due to attached V. convallaria causes significant changes to the flow around aggregates in a dynamic manner and reshapes mass transport boundary layers. Further, we find that activity-mediated local flows along with sinking modify the encounter and plume cross-sections of the aggregate and induce sustained aggregate rotations. Overall, our work shows the important role of biological activity in shaping the near-field flows around aggregates with potentially important effects on aggregate fate and material fluxes. 

Sinking or sedimentation of biological aggregates plays a critical role in carbon sequestration in the ocean and in vertical material fluxes in waste-water treatment plants. In both these contexts, the sinking aggregates are “active,” since they are biological hot-spots and are densely colonized by microorganisms including bacteria and sessile protists, some of which generate feeding currents. However, the effect of these feeding currents on the sinking rates, trajectories, and mass transfer to these "active sinking particles," has not previously been studied. Here we use a novel scale-free vertical-tracking microscope (a.k.a. Gravity Machine, Krishnamurthy et al. "Scale-free vertical tracking microscopy." Nature Methods (2020)) to follow model sinking aggregates (agar spheres) with attached protists (Vorticella convallaria), sinking over long distances while simultaneously measuring local flows. We find that activity due to attached \vortc causes significant changes to the flow around aggregates in a dynamic manner and reshapes mass transport boundary layers. Furthermore, we find that activity-mediated local flows along with sinking modify the encounter and plume cross-sections of the aggregate and induce sustained aggregate rotations. Overall our work shows the important role of biological activity in shaping the near-field flows around aggregates with potentially important effects on aggregate fate and material fluxes.