local alignment search tool algorithm  (Biotechnology Information)

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: Video showing animal behavior during the social memory task alongside the corresponding miniscope calcium recording from the same session. Calcium-active ROIs are pseudocolored according to their Neuroplex-assigned fluorophore identity, indicating the corresponding projection-defined neuronal population.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques:

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a ) Surgical paradigm. In a TetO-GCaMP6s × CaMKII-tTa mouse, 9 AAV retro viruses are injected into downstream brain regions and gradient-index (GRIN) lens implanted into the target region. ( b ) Simultaneous recording of GCaMP6s (top) and behavior (bottom) during a social memory task. Scale bar = 100 µm ( c ) GCaMP6s recordings are processed. Constrained non-negative matrix factorization (CNMF)-defined ROIs (top) and ΔF/F traces (bottom) are exported. Scale bar = 100 µm. ( d ) Mice are head fixed and FOV under the GRIN lens imaged using the multiplexed lambda method. ( e ) Transformations are determined using anatomical background images to co-register the two imaging platforms. The transformations are applied to CNMF-defined ROIs. Scale bar = 100 µm. ( f ) Multispectral data are collected for each ROI (top) and an average spectral fingerprint for all ROIs is generated (bottom). Mean ±1.5 SD. Scale bar = 100 µm. ( g ) A linear unmixing model is applied to determine the fluorophore contribution for each ROI. Scale bar = 100 µm. ( i ) Neural activity is sorted by cell type. Scale bars = 20 ΔF/F (vertical), 20 s (horizontal).

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: Injection, Imaging, Generated, Activity Assay

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a ) In vivo multiplexed spectral imaging paradigm. Schematic of multiplexed spectral imaging (left). Depiction of overlapping fluorophore spectral emissions for each excitation laser wavelength (middle). Depiction of multiplexed spectral images which create a 204-dimensional dataset (right). ( b ) Automated co-registration of miniscope and laser scanning confocal microscope (LSM) images. Top: A calibration slide used to measure scaling between modalities. Bottom: Experimental FOV showing brain vasculature. Miniscope and confocal images of the same FOV and automated co-registration overlay with zoomed-in regions of interest. ( c ) Example calcium-activity regions of interest (ROI) derived from miniscope data co-registered and overlaid on confocal LSM image. ( d ) Spectral fingerprint of the example ROI, with the solid blue line showing the example ROI and the dashed line depicting the average spectral profile of the animal. ( e ) Beta multiplier from the example ROI, depicting the deviation from the mean beta value for all ROIs from the same animal. ( f ) Empirically measured spectral profiles from pure fluorophore samples, shown as beta-weighted contributors to ROI fingerprints. Scale bar: 100 µm ( a, b ), 10 µm ( b inset and c ).

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: In Vivo, Imaging, Microscopy, Activity Assay, Derivative Assay

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a–e ) Average spectral fingerprints computed across all regions of interests (ROIs) for each experimental mouse. Mean ±1.5 × SD ( f–j ) Spatial distribution of ROIs and their corresponding fluorophore assignments overlaid on anatomical images from the same mouse. Scale bars = 100 µm.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques:

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a–r ) Each panel shows an regions of interest (ROI) that exceeded threshold for a single fluorophore identity assignment. (Left:) Functional ROIs identified from miniscope recordings during behavior, co-registered and overlaid on corresponding in vivo confocal images. (Center:) Spectral fingerprint of the ROI (solid line), compared to the animal’s average spectral background (dashed line). Excitation-emission bins are color-coded to excitation laser wavelength. Right inset: Beta multiplier values for all fluorophores from the same ROI, plotted as standard deviations above the animal-specific baseline. Scale bars = 10 µm.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: Functional Assay, In Vivo

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a–h ) Simulated single-fluorophore datasets were used to assess Neuroplex classification performance under conditions designed to mimic common experimental noise sources. Each panel shows a schematic of the modeling setup (inset), average identification accuracy for single-pass (gray) and dual-pass (black) classification (top), and a breakdown of classification outcomes by fluorophore, including correct identifications, false-negatives, and false-positives (bottom). Each simulation contained 250 regions of interests (ROIs) and was repeated 100 times. Modeled perturbations included: increasing GCaMP background within ROIs ( a–b ), added low-level spectral background from other fluorophores ( c–d ), decreased signal-to-noise ratio via Gaussian white noise ( e–f ), and over-representation of a single fluorophore in the population ( g–h ). ( i ) Comparison of theoretical equal beta contributions (dashed line) to empirically observed beta weights across fluorophores in an example animal, showing deviations that inform the need for adjusted thresholds. ( j ) Example ROI illustrating how second-pass analysis recovers fluorophore assignments missed during initial thresholding. ( k ) Simulated experimental condition using known ROI spectra matched to the actual fluorophore distribution in animal YAS21272R. Background fluorescence, GCaMP co-expression, and white noise were added to approximate in vivo signal characteristics. ( l ) Final classification breakdown for the modeled condition in ( k ) using the dual-pass approach.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: Comparison, Fluorescence, Expressing, In Vivo

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a ) Modeled conditions with ROIs containing either single or dual fluorophores: depiction (left), breakdown of dual-pass analysis performance per fluorophore pair (right). ( b ) Modeled experimental conditions with ROIs containing either single or dual fluorophores with added experimental background, GCaMP background, and white noise: depiction (left), breakdown of dual-pass analysis performance (right). Bars reflect percent of ROIs where each fluorophore was correctly identified (colored), misidentified (white), or not identified (gray). ROIs n=460, replicates n=100.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques:

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a ) Identified fluorophores for injection paradigm A. Viral injection paradigm A consisted of mTagBFP2 into the dorsal periaqueductal gray (dPAG), mTurquoise2 into the basolateral amygdala (BLA), T-Sapphire into the claustrum (Cla), mVenus into the nucleus accumbens (NAc), mOrange2 into the striatum (Str), mScarlet into the locus coeruleus (LC), FusionRed into the ventral tegmental area (VTA), mCyRFP1 into the lateral habenula (LHb), and mNeptune2.5 into the contralateral prefrontal cortex (c-mPFC) (left). Spatial distribution of ROIs and respective fluorophore matches overlaid on anatomical images from the same mouse (right). Distribution of identified fluorophores per mouse (inset). ( b ) Identified fluorophores for injection paradigm B. Viral injection paradigm B consisted of mTagBFP2 into the c-mPFC, mTurquoise2 into the LHyp, T-Sapphire into the Str, mVenus into the VTA, mOrange2 into the LC, mScarlet into the dPAG, FusionRed into the BLA, mCyRFP1 into the NAc, and mNeptune2.5 into the Cla (left). Spatial distribution of ROIs and respective fluorophore assignments overlaid on the anatomical image from the same mouse (right). Distribution of identified fluorophores per mouse (inset). Color and letter codes for fluorophores and injection regions, respectively (far right). ( c ) Percentage of identified ROIs with a fluorophore match. Animal n=5; ROI n=1,327. ( d ) Percent of cells identified for each fluorophore. Letter insets on individual data points correspond to injected regions. N=1072. One-way ANOVA p =0.0071. ( e ) Percent of cells identified for each injected region. Number insets on individual data points correspond to injected fluorophore. N=1072. Mean ± SEM. One-way ANOVA, p =0.2599. Scale bars: 100 µm.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: Injection

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a ) Proportion of functionally defined ROIs classified as expressing one or two fluorophores based on thresholded beta multipliers. ( b ) Frequency of dual fluorophore assignments across the dataset. Left: Heatmap showing co-assignment rates between fluorophore pairs. Right: Total frequency of dual hits per individual fluorophore. ( c ) Frequency of dual-labeled ROIs by brain region. Left: Heatmap showing co-occurrence between projection-defined populations. Right: Total frequency of dual hits per primary brain region. (n=1327 ROIs) ( d ) Example ROIs from miniscope imaging co-registered with confocal lambda stacks. ROIs are overlaid on three excitation channels (405, 561, 639 nm). ( e ) Z-scored beta multipliers across all fluorophores for each example ROI, with above-threshold values circled. Dual fluorophores were assigned to two ROIs (28, 98), and only a single fluorophore to ROI 142. ( f ) Spectral fingerprints for each example ROI (solid line) plotted against the average background spectrum for the animal (dashed line). ROI 28 (top) was assigned two spectrally distinct fluorophores (mTagBFP2+mNeptune2.5); ROI 98 (middle) shows co-assignment of more spectrally overlapping fluorophores (mOrange2+mNeptune2.5); ROI 142 (bottom) is included as a single-label example with a strong match to mTagBFP2 only. Scale bars = 10 µm.

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: Expressing, Labeling, Imaging

Journal: eLife

Article Title: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

doi: 10.7554/eLife.110277

Figure Lengend Snippet: ( a ) Schematic of experimental design: Four retrograde fluorophores were injected into distinct brain regions of GCaMP6s transgenic mice to label projection-defined pyramidal neurons in the medial prefrontal cortex (mPFC). mTagBFP2 was injected into the contralateral prefrontal cortex (c-mPFC), mVenus into the striatum (Str), mOrange2 into the claustrum (Cla), and mNeptune2.5 into the ventral tegmental area (VTA). ( b–c ) Spatial distribution of identified regions of interests (ROIs) overlaid on anatomical reference images (left) and corresponding average spectral fingerprints from each experimental animal (right). Shaded regions represent ±1.5 std from the mean. Scale bars 100 µm. ( d ) Proportion of ROIs classified as single- or dual-labeled based on dual-pass thresholding. Animal n=2; ROI n=289. ( e ) Percent of identified ROIs assigned to each individual fluorophore. ( f ) Frequency of dual-fluorophore assignments across the dataset. Left: Heatmap showing pairwise co-occurrence rates between fluorophore (and region) combinations. Right: Total frequency of dual hits per individual fluorophore/region. ( g–h ) Modeled experimental conditions assessing classification accuracy in either single-fluorophore ( g ) or dual-fluorophore ( h ) expression contexts. Spectra were simulated with empirical fluorophore distributions, real background, GCaMP contamination, and Gaussian white noise (left). Breakdown of error types (right). ROIs n=460, replicates n=100. ( i ) Fluorophore interaction analysis showing classification accuracy when fluorophores were co-expressed. Bars indicate the percentage of ROIs correctly identified (colored), missed (gray: false negatives), or incorrectly labeled (white: false positives).

Article Snippet: To determine the accuracy and robustness of our fluorophore identification algorithm component of Neuroplex, we validated the algorithm using a series of simulated datasets based on real spectral fingerprints.

Techniques: Injection, Transgenic Assay, Labeling, Expressing