Several super-resolution techniques exist, yet most require multiple lasers, use either large or weakly emitting fluorophores, or involve chemical manipulation. Here we show a simple technique that exceeds the standard diffraction limit by 5–15x on fixed samples, yet allows the user to localize individual fluorophores from among groups of crowded fluorophores. It relies only on bright, organic fluorophores and a sensitive camera, both of which are commercially available. Super-resolution is achieved by subtracting sequential images to find the fluorophores that photobleach (temporarily or permanently), photo-activate, or bind to the structure of interest in transitioning from one frame to the next. These fluorophores can then be localized via Gaussian fitting with selective frame averaging to achieve accuracies much better than the diffraction limit. The signal-to-noise ratio decreases with the square root of the number of nearby fluorophores, producing average single-molecule localization errors that are typically < 30 nm. Surprisingly, one can often extract signal when there are approximately 20 fluorophores surrounding the fluorophore of interest. Examples shown include microtubules (in vitro and in fixed cells) and chromosomal DNA.
To measure nanometric features with super-resolution requires that the stage, which holds the sample, be stable to nanometric precision. Herein we introduce a new method that uses conventional equipment, is low cost, and does not require intensive computation. Fiduciary markers of approximately 1 µm x 1 µm x 1 µm in x, y, and z dimensions are placed at regular intervals on the coverslip. These fiduciary markers are easy to put down, are completely stationary with respect to the coverslip, are bio-compatible, and do not interfere with fluorescence or intensity measurements. As the coverslip undergoes drift (or is purposely moved), the x-y center of the fiduciary markers can be readily tracked to 1 nanometer using a Gaussian fit. By focusing the light slightly out-of-focus, the z-axis can also be tracked to < 5 nm for dry samples and <17 nm for wet samples by looking at the diffraction rings. The process of tracking the fiduciary markers does not interfere with visible fluorescence because an infrared light emitting diode (IR-LED) (690 and 850 nm) is used, and the IR-light is separately detected using an inexpensive camera. The resulting motion of the coverslip can then be corrected for, either after-the-fact, or by using active stabilizers, to correct for the motion. We applied this method to watch kinesin walking with ≈8 nm steps.
With a rising incidence of COVID-19–associated morbidity and mortality worldwide, it is critical to elucidate the innate and adaptive immune responses that drive disease severity. We performed longitudinal immune profiling of peripheral blood mononuclear cells from 45 patients and healthy donors. We observed a dynamic immune landscape of innate and adaptive immune cells in disease progression and absolute changes of lymphocyte and myeloid cells in severe versus mild cases or healthy controls. Intubation and death were coupled with selected natural killer cell KIR receptor usage and IgM+ B cells and associated with profound CD4 and CD8 T-cell exhaustion. Pseudo-temporal reconstruction of the hierarchy of disease progression revealed dynamic time changes in the global population recapitulating individual patients and the development of an eight-marker classifier of disease severity. Estimating the effect of clinical progression on the immune response and early assessment of disease progression risks may allow implementation of tailored therapies.
The COVID-19 epidemic of 2019–20 is due to the novel coronavirus SARS-CoV-2. Following first case description in December, 2019 this virus has infected over 10 million individuals and resulted in at least 500,000 deaths world-wide. The virus is undergoing rapid mutation, with two major clades of sequence variants emerging. This study sought to determine whether SARS-CoV-2 sequence variants are associated with differing outcomes among COVID-19 patients in a single medical system. Whole genome SARS-CoV-2 RNA sequence was obtained from isolates collected from patients registered in the University of Washington Medicine health system between March 1 and April 15, 2020. Demographic and baseline clinical characteristics of patients and their outcome data including their hospitalization and death were collected. Statistical and machine learning models were applied to determine if viral genetic variants were associated with specific outcomes of hospitalization or death. Full length SARS-CoV-2 sequence was obtained 190 subjects with clinical outcome data. 35 (18.4%) were hospitalized and 14 (7.4%) died from complications of infection. A total of 289 single nucleotide variants were identified. Clustering methods demonstrated two major viral clades, which could be readily distinguished by 12 polymorphisms in 5 genes. A trend toward higher rates of hospitalization of patients with Clade 2 infections was observed (p = 0.06, Fisher’s exact). Machine learning models utilizing patient demographics and co-morbidities achieved area-under-the-curve (AUC) values of 0.93 for predicting hospitalization. Addition of viral clade or sequence information did not significantly improve models for outcome prediction. In summary, SARS-CoV-2 shows substantial sequence diversity in a community-based sample. Two dominant clades of virus are in circulation. Among patients sufficiently ill to warrant testing for virus, no significant difference in outcomes of hospitalization or death could be discerned between clades in this sample. Major risk factors for hospitalization and death for either major clade of virus include patient age and comorbid conditions.
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