Optimal Intramuscular Injection Site and Maximum Volume in Adult Population

Intramuscular (IM) injections are a common, yet complex technique used to deliver medication into the muscles of the body. More than 12 billion IM injections are administered annually throughout the world (Jin et al., 2015). Unsafe injection practices can lead to further complications, such as “abscess, hematoma, ecchymosis, pain, and vascular and nerve injury” (Potter et. al., 2020, p.633). The choice of an injection site and needle length varies based on the volume to be administered, size of the patient’s muscle, and the patient’s body mass index (BMI). With the proper education, utilizing the best injection technique and optimal site limits further patient complications and provides positive outcomes

Hit or miss? Impact of time series resolution on resolving phytoplankton dynamics at hourly, weekly, and satellite remote sensing frequencies

Characterizing marine phytoplankton community variability is crucial to designing sampling strategies and interpreting time series. Satellite remote sensing, microscopy sampling, and flow through imaging systems have widely different resolutions: from weekly or monthly with microscopy sampling to daily when no cloud cover or glint is present with polar-orbiting satellites, and hourly for autonomous imaging instruments. To improve our understanding of data robustness against sampling resolution at different taxonomic levels, we analyze 2 yr of data from an Imaging FlowCytobot with hourly resolution and resample it to daily, satellite-temporal, and weekly microscopy sampling resolution. We show that weekly and satellite-temporal resolutions are sufficient to resolve general community composition but that the randomness of satellite-temporal resolution can result in overrepresenting or underrepresenting certain categories. While the yearly phytoplankton biomass bloom is detected in late winter by all four resolutions, category-specific yearly blooms are generally consistent in timing but often underestimated or missed by the weekly and satellite-temporal resolutions, introducing a bias in year-to-year comparisons. A minimum of biweekly sampling, particularly during known bloom periods, would lower the bias in such categories. Similarly, sampling time should be considered as daily variations are category-specific. Overall, morning and low tide sampling tended to have higher biomass. We provide tables for categories detected by the IFCB in Narragansett Bay with their major bloom characteristics and recorded daily variability to inform future sampling designs. These results provide tools to interpret past and future time series, including possible detection of specific taxonomic groups with targeted satellite algorithms

A live-cell platform to isolate phenotypically defined subpopulations for spatial multi-omic profiling.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype-such as invasiveness, cell:cell interactions, and changes in spatial positioning-with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations

3D spheroid invasive area and circularity quantification.

3D spheroid invasive area and circularity quantification.</p

Example photoconversion in different cell culture conditions.

a. Cells stably expressing a photoconvertible tag (ex: H2B-Dendra2, Pal-Dendra2) can be prepared under non-adherent, 3D, or 2D experimental conditions which illicit distinct and imageable cellular response for photoconversion. Non-adherent conditions were performed with RPMI8226 myeloma cells; H1299 lung cancer cells were used for all other conditions. Scale bar, 50 μm. b, c. Integrated density (relative fluorescence units) quantification of 6 or more cells pre- and post- photoconversion in the green (b) and red (c) channels, emission peaks, 507 nm, and 573 nm, respectively. d, e. Quantification of integrated density percent change of 6 or more cells pre- and post- photoconversion in the green (d) and red (e) channels.</p

SaGA schematic to isolate distinct cell(s) based upon live, user-defined phenotypic criteria.

Schematic showing three broad steps of SaGA: 1) Preparation, 2) Selection and isolation, and 3) Analysis. SaGA can be applied to a variety of cell conditions, such as non-adherent, 3-dimensional (3D), and 2-dimensional (2D), for selection, isolation, and analysis of live subpopulations within a parental population. Cells stably expressing a photoconvertible tag can be precisely photoconverted (from green to red) based upon live, user-defined, phenotypic criteria. These red photoconverted cells are then isolated utilizing fluorescence activated cell sorting (FACS) for multi-omic analysis and/or cell cultivation for long-term in vitro and in vivo analyses. Created with Biorender.com.</p

Potential loss of heterogeneity and error sources and measures to minimize them.

Cellular loss of heterogeneity can occur during sample preparation, selection and isolation, and analysis. Listed is each major stage of SaGA with potential problems (bulleted above image within each panel) that can occur and respective potential solutions (bulleted below image within each panel). Graphical images created with Biorender.com.</p

Example downstream applications of SaGA-isolated subpopulations.

Example downstream applications of SaGA-isolated subpopulations.</p

SaGA workflow.

Each panel provides an example of a major component of SaGA: Preparation, Selection and isolation, and Analysis. a. 3D spheroid invasion assay set-up beginning with spheroid formation in a low adherence 96-well plate to embedment and invasion in recombinant basement membrane. Scale bar, 250 μm. b. Dendra2 visualization under non-adherent, 3D and 2D conditions. 2D conditions are shown utilizing both nuclear- (H2B-Dendra2) and membrane- (Pal-Dendra2) localized protein tags. Scale bar, 50 μm. c. Defining a region of interest (ROI) (white circle) for cell selection and photoconversion. Scale bar, 50 μm. d. Matrix degradation in 3D conditions utilizing collagenase/dispase cocktail. e. FACS plot showing non-photoconverted (-) and photoconverted (+) cells. f. 3D spheroid invasion assay with H1299 parental population and SaGA-isolated leader and follower subpopulations. Scale bar, 250 μm. g. Invasive area and spheroid circularity quantification. *p < 0.05 by one-way ANOVA with Tukey’s multiple comparisons test.</p

Troubleshooting table.

Numerous techniques have been employed to deconstruct the heterogeneity observed in normal and diseased cellular populations, including single cell RNA sequencing, in situ hybridization, and flow cytometry. While these approaches have revolutionized our understanding of heterogeneity, in isolation they cannot correlate phenotypic information within a physiologically relevant live-cell state with molecular profiles. This inability to integrate a live-cell phenotype—such as invasiveness, cell:cell interactions, and changes in spatial positioning—with multi-omic data creates a gap in understanding cellular heterogeneity. We sought to address this gap by employing lab technologies to design a detailed protocol, termed Spatiotemporal Genomic and Cellular Analysis (SaGA), for the precise imaging-based selection, isolation, and expansion of phenotypically distinct live cells. This protocol requires cells expressing a photoconvertible fluorescent protein and employs live cell confocal microscopy to photoconvert a user-defined single cell or set of cells displaying a phenotype of interest. The total population is then extracted from its microenvironment, and the optically highlighted cells are isolated using fluorescence activated cell sorting. SaGA-isolated cells can then be subjected to multi-omics analysis or cellular propagation for in vitro or in vivo studies. This protocol can be applied to a variety of conditions, creating protocol flexibility for user-specific research interests. The SaGA technique can be accomplished in one workday by non-specialists and results in a phenotypically defined cellular subpopulations for integration with multi-omics techniques. We envision this approach providing multi-dimensional datasets exploring the relationship between live cell phenotypes and multi-omic heterogeneity within normal and diseased cellular populations.</div