Standard fluorescence microscopy approaches rely on measurements at single excitation and emission bands to identify specific fluorophores and the setting of thresholds to quantify fluorophore intensity. This is often insufficient to reliably resolve and quantify fluorescent labels in tissues due to high autofluorescence. Here we describe the use of hyperspectral analysis techniques to resolve and quantify fluorescently labeled cells in highly autofluorescent lung tissue. This approach allowed accurate detection of green fluorescent protein (GFP) emission spectra, even when GFP intensity was as little as 15% of the autofluorescence intensity. GFP-expressing cells were readily quantified with zero false positives detected. In contrast, when the same images were analyzed using standard (single-band) thresholding approaches, either few GFP cells (high thresholds) or substantial false positives (intermediate and low thresholds) were detected. These results demonstrate that hyperspectral analysis approaches uniquely offer accurate and precise detection and quantification of fluorescence signals in highly autofluorescent tissues.

Genomic data provides useful information for public health practice, particularly when combined with epidemiologic data. However, sampling bias is a concern because inferences from nonrandom data can be misleading. In March 2021, the Washington State Department of Health, USA, partnered with submitting and sequencing laboratories to establish sentinel surveillance for SARS-CoV-2 genomic data. We analyzed available genomic and epidemiologic data during presentinel and sentinel periods to assess representativeness and timeliness of availability. Genomic data during the presentinel period was largely unrepresentative of all COVID-19 cases. Data available during the sentinel period improved representativeness for age, death from COVID-19, outbreak association, long-term care facility–affiliated status, and geographic coverage; timeliness of data availability and captured viral diversity also improved. Hospitalized cases were underrepresented, indicating a need to increase inpatient sampling. Our analysis emphasizes the need to understand and quantify sampling bias in phylogenetic studies and continue evaluation and improvement of public health surveillance systems.

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