Flow Cytometry Facility The Panel Design Tool

Transcription

1 The Automated design of multicolor flow cytometry panels

2 Contents The antibody fluorophore panel Parameters & desired properties The What it can do The input needed Input processing Panel creation Output visualization Functionality summary and future implementations

3 Flow Cytometry Panels Antibodies Fluorophores Cell types, markers of interest + Detection bdbiosciences.com

4 Flow Cytometry Panels Matching markers with fluorophores and instrument channels for optimal detection. For the best resolution: Minimize spillover Fluorophore emission Optical filters Distribution of fluorophores on cell types Equalize signal intensity Fluorophore brightness Marker expression levels Antibody quality Challenge: Large number of combinations (6 markers: 720, 28 markers: ) Strong interplay of parameters

5 Computation Tool for Designing Panels Input: What do you want to measure (cells & markers) How do you want to measure (instrument & possible dyes) Output: Spillover optimized panel propositions

6 : Calculation Steps Calculation of dye properties for specific instruments based on spectra. Instrument specific brightness Ranking of dyes by generated spillover Marker ranking according to co-occurrence with other markers. Initial matching of markers and dyes based on expression levels and dye brightness. Sampling of maker dye assignments and sorting based on spillover criteria.

7 Input: Cell Types and Markers Example: The HIPC T-cell panel Finak et al., Scientific Reports 2016 Identification of 10 cell types using 7 markers.

8 Input: Cell Types and Markers Input format: Expression levels from Bausch-Fluck et al., PLOS 2015 Options for expression levels:? unknown level -> set to range from lowest to highest +, ++, relative level expression level range

9 Input Processing: Cell Types and Markers Markers: Cell Types: Markers are ranked according to their number of co-occurrences with other markers. Cell types are ordered by number of markers.

10 Input: Optical Layout of the Instrument José Duarte Instrument detection channels are calculated based on model functions of the LP and BP optical filters for each excitation wavelength.

11 Input: Instrument Example José Duarte USZ The optical layout (lasers, optical filters and detection channels) is provided as an Excel sheet.

12 Input: Dyes List of dyes that can be used for detection. For each dye the Excitation spectrum Emission spectrum Relative brightness is required.

13 Input Processing: Dyes Excitation Emission Amount of Dye absorbance at each instrument excitation wavelength Calculation of fluorescence signal in each instrument channel scaled by relative excitation and brightness.

14 Input Processing: Dyes For each dye the following is calculated: Fluorescence signal for each channel, scaled by relative excitation and brightness. Main channel instrument channel that detects largest part of dye fluorescence Spillover score sum of detected fluorescence in all channels except the main channel relative to the main channel signal Relative brightness of the dye as measured on the instrument Fraction of total dye emission detected in the main channel The dyes are ranked by their spillover score.

15 Input Processing: Dyes Summary of processed dye information is provided for the user to facilitate initial selection of dyes to use in the panel.

16 Input: Dyes Excel file containing: Free LD Marker X Marker Y. AF488 LD_Aqua AF488 BUV395 AF647 LD_Blue APC AF660 LD_FarRed BV421 AF680 LD_Green AF700 LD_NIR Free: dyes that can be used in the panel LD: live/dead stains (optional) Marker X: example for a restricted assignment (optional) Marker Y: example for a fixed assignment (optional)

17 Input Processing: Dyes for Markers Each marker receives a list of suitable dyes. To equalize signal levels, marker expression level is matched with dye brightness (as measured on the instrument). Decreasing # of co-occurrences Increasing spillover score CD3 CD8 CD4 CCR7 CD45RA CD38 HLADR PerCP-Cy5.5 PE-Cy7 Cy3 AF488 AF488 APC PE-Cy7 PE-Cy5 PE-Cy5 Cy3.4 AF647 AF647 PE-Cy5.5 PE-Cy5 PE-Cy7 PE PG FITC FITC PerCP PE PE APC V500 PG PG PE APC

18 Assignment Procedure Cell type with most markers Marker with highest number of cooccurrences Next marker Restart yes no Fixed assignment for marker? yes Unassigned marker left? no Sampled m times? no yes Choose dye for marker randomly from the top n suitable dyes for that marker. Remove dyes using the same detection channel as the chosen dye from other dye lists. Calculate spillover to marker signal ratio for each sample Parameters Number of dyes to choose from (n) Sampling number per cell type (m) Number of panels to work with (k) k different panels Repeat process for unassigned markers on remaining cell types keeping only best assignment. Keep best k samples

19 Assignment Example Increasing spillover score T-cell CD8 activated Decreasing # of co-occurrences CD3 CD8 CD38 HLADR PerCP-Cy5.5 PE-Cy7 APC PE-Cy7 PE-Cy5 PE-Cy5 PE-Cy5.5 PE-Cy5 PE-Cy7 PE PerCP PE PE APC PE APC APC PE-Cy5.5 AF488 PE-Cy5.5 CD8 and HLADR have same expression level -> same dyes to chose from.

20 Assignment Example T-cell CD8 activated Decreasing # of co-occurrences Increasing spillover score CD3 CD8 CD38 HLADR PerCP-Cy5.5 PE-Cy7 APC PE-Cy7 PE-Cy5 Dye is PE-Cy5 chosen from PE-Cy5.5 PE-Cy5 PE-Cy7 the best PE n dyes (5 PerCP PE PE in this example). APC PE APC APC PE-Cy5.5 AF488 PE-Cy5.5

21 Assignment Example T-cell CD8 activated Decreasing # of co-occurrences Increasing spillover score CD3 CD8 CD38 HLADR PerCP-Cy5.5 PE-Cy7 APC PE-Cy7 PE-Cy5 PE-Cy5 PE-Cy5.5 PE-Cy5 PE-Cy7 PE PerCP PE PE APC PE APC APC PE-Cy5.5 AF488 PE-Cy5.5 Chosen dye is removed from all following lists

22 Assignment Example T-cell CD8 activated Decreasing # of co-occurrences Increasing spillover score CD3 CD8 CD38 HLADR APC PE-Cy7 PE-Cy5.5 PE-Cy7 PE-Cy5 PerCP PE-Cy5 PE PE PE PE-Cy5.5 AF488 PE-Cy5.5 PerCP Dyes are also removed when using the same detection channel as the chosen dye.

23 Assignment Example T-cell CD8 activated Decreasing # of co-occurrences Increasing spillover score CD3 CD8 CD38 HLADR APC PerCP PE PE-Cy7 AF488 PE FITC PB PE-CF594 When less than the desired number of dyes is in the list, dye is chosen from remaining dyes. When only one dye left, that dye is assigned to marker. If list is empty, it is refilled from remaining free dyes.

24 Assignment Example T-cell CD8 activated CD3 CD8 CD38 HLADR APC PerCP FITC PE Assignment for cell type 1 repeated m times, best k combinations kept. T-cell CD4 activated CD3 CD4 CD38 HLADR APC Cy3 FITC PE Cy3.5 PG Procedure repeated for next cell type, keeping already assigned markers fixed.

25 Assignment Example Result: m different panels Panel 1 Panel 2 Panel 3 CD3 APC PE-Cy7 PE-Cy7 CD8 PerCP APC APC CD4 V450 PacificBlue PacificBlue CCR7 PE-Cy7 PE-CF594 PE-CF594 The different panels can be sorted and selected by their overall spillover scores and further selected by the predicted resolution for certain markers.

26 Choosing a Panel 1) Select the best panels by total spillover score. 2) Look at the spillover scores for each marker on each cell type. 3) Also check the estimated signal levels. 4) Select panel that best suits your experimental question.

27 A Spectra Viewer Look at Individual Panels Generate spectra viewer like plots for a panel: Plots are made for each cell type and for each excitation wavelength.

28 Running the PDT in R Generating the dye library for a specific instrument: make_dye_library(optical_filters, dye_spectra, brightness_data, instrument_name) Calculates dye emission detected on instrument, spillover and brightness. Processing the input: input_dat <- process_input(experiment_layout, dyes_for_markers, dye_library, min_nr_dyes) Processes the user input, ranks markers and dyes, does brightness expression level matching etc.

29 Running the PDT in R Generating the panels (stochastic search): panels <- make_panels(input_dat, nr_dyes, nr_sampling, nr_panels) nr_dyes: number of dyes to randomly choose from during assignment nr_sampling: number of times the assignment is repeated for one cell type nr_panels: number of panels generated To get a reference value for the spillover score of a panel layout, run the assignment first with nr dyes, sampling and panels set to 1. Increase parameters to sample more combinations and get higher chance for good results (can take a while).

30 Running the PDT in R Generating the panels (thorough search): panels <- make_panels(input_dat, search_all = TRUE) Tries to test all possible dye marker combinations. If no marker dye restriction lists are given, dyes tested are taken from the marker expression levels dye brightness matching lists. For larger panels with many possibilities the computational costs will be huge! Estimate the number of possible combinations beforehand and do not run searches with more than a couple hundred thousands possibilities.

31 Running the PDT in R Get the total spillover score of the panels: get_total_so(panels) Select the best panels by their spillover score: panel_selection <- get_best_by_so(panels, nr_selection) Save dye marker assignments to excel file: write.assignment(panel_selection, file_name) Save detailed spillover information of each panel to excel file: write.panels(panel_selection, file_name) Generate the spillover per marker plots: plot_spillover(panel_selection, file_name, input_dat) Generate the spectra viewer plot for one panel: plot_spectra(output_name, panel_selection, panel_nr, filter_list, input_dat)

32 Running the PDT in R A preliminary graphical user interface is available for a simple workflow. The full functionality is currently only available from the R console.

33 Feature Summary User Essentials Input needed: Instrument configuration and dye properties (provided by the FCF for all facility machines) Experiment layout with cell types, markers and expression levels (template provided) List of available dyes / restrictions / fixed assignments (template provided) What you get: List of panel propositions ready to be tested Spillover calculations and visualization for each marker on each cell type

34 Future Development Give us your feedback and send us your wishes for new features! Read in expression level data (if available). Get our own data for dye brightness. Proper documentation.