Hdl73 Signal Isolation

HDL73 Signal Isolation - Research Notes

Experiment Overview

Context

HDL73 spleen sample from HiperGator had 28 signal markers but only 5 had been manually signal-isolated (CD11c, CD20, CD3e, CD4, CD8). The remaining 23 markers needed autofluorescence subtraction before the OME-TIFF could be rebuilt with all channels. Existing param files covered 9 channels (including the 5 already done), so 19 channels needed auto-analysis.

Verified Workflow

1. Channel-to-blank mapping from channelnames.txt

The Phenocycler cycle layout in channelnames.txt encodes 3 positions per cycle (a/b/c). Blanks at cycles 1 and 13 provide matched autofluorescence references:

POSITION_A = ["CD20", "CD31", "CD34", "CD35", "Lyve1", "PanCK", "SMActin"]
POSITION_B = ["CD8", "CD15", "CD21", "CD44", "CD45RO", "CD5", "CollagenIV", "ECAD", "FoxP3", "Ki67", "Podoplanin"]
POSITION_C = ["CD3e", "CD4", "CD11c", "CD107a", "CD163", "CD1c", "CD45", "CD68", "HLADR", "Vimentin"]

2. Blank averaging

blank_avg = ((blank1.astype(np.float32) + blank13.astype(np.float32)) / 2.0).astype(np.uint16)

3. Parameter resolution (file or auto)

from kintsugi.signal import analyze_for_subtraction, subtract_autofluorescence, compute_subtraction_quality

# If param file exists, parse blank_clip_factor and background_scale_factor
# If not, auto-analyze:
params = analyze_for_subtraction(signal, blank_avg, tissue_type="spleen", marker_name=name)

4. Subtraction and quality check

result = subtract_autofluorescence(signal, blank_avg,
    blank_clip_factor=params["blank_clip_factor"],
    blank_scale_factor=params["blank_scale_factor"])
quality = compute_subtraction_quality(signal, result, blank_avg)

5. OME-TIFF rebuild

python scripts/convert_to_ome_tiff.py FromHipergator/HDL73_SPL_Processed/ImageJ Images/HDL073_PC29.ome.tiff

Failed Attempts (Critical)

Final Parameters

Channels with existing param files

CD11c:  clip=7000,  scale=1.4
CD15:   clip=9000,  scale=0.7
CD1c:   clip=5000,  scale=1.8  # failed marker
CD20:   clip=7000,  scale=0.2
CD21:   clip=3000,  scale=1.6
CD3e:   clip=12000, scale=1.9  # param file also contains ImageJ macros
CD4:    clip=7000,  scale=2.0
CD5:    clip=10000, scale=1.4  # failed marker
CD8:    clip=8000,  scale=1.5

Blank medians by position

position_a: 14526  # highest autofluorescence
position_b: 6708
position_c: 4341   # lowest autofluorescence

Output

• 29 files in ImageJ/ (28 signal + DAPI copy)
• OME-TIFF: 2.69 GB, pyramidal 5-level, 512x512 tiles, DEFLATE

Key Insights

• CD3e param file contained ImageJ macro commands mixed with subtraction params — must parse carefully, only extract blank_clip_factor and background_scale_factor
• DAPI requires no subtraction — just copy directly
• analyze_for_subtraction() with tissue_type="spleen" works well for most channels but PanCK, Podoplanin, SMActin got low signal preservation (<0.3) — may need manual parameter tuning
• CD1c and CD5 were listed in Failed_markers.txt — processed anyway but results should be visually validated
• Position a blanks have ~3x higher median than position c — blank intensity varies significantly across positions

References

• scripts/process_hdl73_channels.py — the processing script
• scripts/convert_to_ome_tiff.py — OME-TIFF builder
• FromHipergator/HDL73_SPL_meta/channelnames.txt — cycle layout source
• FromHipergator/HDL73_SPL_Processed/Processing_parameters/ — existing param files