Search BANC EM with an LM colour-depth MIP (SRENs)

Source: vignettes/search_banc_with_lm.Rmd

Goal

Take a 3-D light-microscopy image of a candidate SREN (sex-peptide- receptor rectal enteric neuron) and use it as a query against the BANC female adult connectome to recover the matching EM neuron(s). SRENs are a pair of cells at the posterior tip of the abdominal ganglion that express the sex peptide receptor and send efferent processes to the rectum; they are required for the rod-shaped fecal pellets (“RODs”) that mated females produce in response to seminal-fluid sex peptide. See Chae et al., Citations below, for the upstream/downstream circuit context.

The pipeline starts from the raw .lsm (a 40× confocal stack of the abdominal tip with the cord at an angle in the field of view) and lands a clean BANC ranking:

  1. Extract GFP (label) and NC82 (anatomical reference) from the LSM at native voxdims via inst/python/lsm_to_nrrd.py.
  2. PCA pre-rotate the LSM onto JRC2018VNCF axes (inst/python/lsm_pca_rotate.py). The NC82 tissue mask’s principal axes are aligned to template (X, Y, Z); the GFP CoG is anchored to a known abdominal-ganglion reference point so the rotated volume lands at the right tip in template coords. No Elastix optimiser run — the partial FOV / weak metric overlap made the rigid+affine optimiser unreliable, and PCA + an explicit anchor is more robust.
  3. Mask GFP by the NC82 neuropil (inst/python/lsm_neuropil_mask.py). Over half of the raw GFP voxels (52% in our female sample) sit outside the cord neuropil — bleed-through into cuticle, rectal muscle, and background. Restricting to in-neuropil GFP triples the cMIP pixel-match count and promotes the right BANC matches.
  4. Bridge the JRC2018VNCF NRRD into JRC2018VNCU_HR (the NeuronBridge VNC reference grid, 573 × 1119 × 219 voxels @ 0.461 × 0.461 × 0.7 µm) via the Saalfeld lab H5 displacement field shipped by navis-flybrains.
  5. Render a colour-depth MIP of the bridged label channel with neuronbridger::nrrd_to_mip(target_space = "VNC").
  6. Search that MIP against the BANC efferent MIP library (~1,033 flow == "efferent" cells in v888, 782 rendered) using colormip_search().
  7. NBLAST the SREN intensity-weighted dotprops against the full efferent library using nat.nblast::nblast(..., normalised = TRUE) both directions and reporting the fwd+rev mean. Points are bridged from JRC2018VNCF µm into BANC µm via the full elastix JRC2018F→BANC chain (bancr::banc_to_JRC2018F(..., method = "elastix", inverse = TRUE)) — a stack of manual affine + elastix affine + coarse B-spline + fine B-spline. An earlier revision of this vignette used the simpler bancr::jrcvnc2018f_to_banc_tpsreg (a 5710-landmark TPS approximation) but that mapping collapses bilateral signal onto one side of the cord at the abdominal tip; the full elastix chain preserves it.
  8. Render top-K BANC meshes overlaid on the SREN cloud as 2-D nat.ggplot panels in BANC native µm, plus a side-by-side cMIP of the SREN query and the top BANC hit, plus a bilateral-pair overlay for the current best hypothesis (EN00B016).

Why use BANC for this? BANC is the only female adult Drosophila CNS currently reconstructed at synapse resolution (v888 here, brain + VNC). Identifying the EM counterpart of the SRENs gives us their downstream connectivity — the descending input from SAG (Sex Peptide Abdominal Ganglion) neurons, the ascending afferents that signal back to the brain, and the rectal target. Colour-MIP search + NBLAST against BANC is the route to recovering these cells from a single LM volume.

Top-6 BANC efferent matches for the SREN query (cMIP, npmask)
Top-6 BANC efferent matches for the SREN query (cMIP, npmask)

What’s in the package

Neither the source .lsm (~480 MB, private) nor the BANC efferent MIP library (~12 MB across 782 PNGs) ship with neuronbridger. The vignette walks through the download steps; the only artefacts shipped are the rendered figures under inst/images/:

  • banc_colormip_sren_query.png — SREN query MIP in JRC2018VNCU_HR after PCA pre-rotation + neuropil mask.
  • banc_colormip_sren_top_hits.png — top-6 hits from colormip_search() (BANC EM mesh in purple, SREN LM cloud coloured by depth, faded by intensity). Each tile shows a top-down YX view + an XZ side projection. Plotted in BANC native µm; SREN cloud bridged via the full elastix JRC2018F→BANC chain.
  • banc_colormip_sren_nblast_top.png — same layout, ranked by NBLAST fwd+rev mean (normalised) against the full ~1,033-cell efferent library.
  • banc_colormip_sren_en00b016_pair.png — the two BANC cells whose manc_cell_type = EN00B016 (candidate SREN bilateral pair): 720575941680133053 (left, BANC cell_type = EFFabg09, ACh) + 720575941659397456 (right, BANC cell_type = EN00B016, octopamine).
  • banc_colormip_sren_vs_top_hit.png — side-by-side cMIP of the SREN query and the current top BANC cMIP hit (label written dynamically from live SeaTable annotations, since these have shifted over successive proofreading rounds).

A self-contained reproducer is at inst/scripts/banc_colormip_sren.R. The Python helpers it shells out to live under inst/python/: lsm_to_nrrd.py, lsm_build_nc82_mask.py, lsm_pca_rotate.py, lsm_neuropil_mask.py.

Prerequisites 

# R packages
if (!require("remotes")) install.packages("remotes")
remotes::install_github("natverse/neuronbridger")
remotes::install_github("flyconnectome/bancr")    # BANC meshes + meta
remotes::install_github("natverse/nat.ggplot")    # 2-D neuron panels

# External tooling (one-off)
#   * Java 17+ (Saalfeldlab RenderTransformed JAR runtime)
#   * gsutil (Google Cloud SDK), authenticated to BANC's GCS bucket
#   * Python deps for the LSM pipeline:
#       conda activate r-reticulate
#       pip install tifffile SimpleITK scipy scikit-image pillow matplotlib
#
# The Saalfeldlab JRC VNC H5 transforms (~110 MB), via the navis
# `flybrains` Python package:
#
#   reticulate::py_run_string(
#     "import flybrains; flybrains.download_jrc_vnc_transforms()")
#
# This writes JRCVNC2018U_JRCVNC2018F.h5 to ~/flybrain-data/.

Step 1 — From raw LSM to a JRC2018VNCF-aligned GFP NRRD

The raw LSM here is a partial FOV of the abdominal tip with the cord at an angle — the tip is roughly in the middle of the image and the rest of the VNC would be off to the top-right. A whole-VNC Elastix recipe doesn’t work (the optimiser keeps falling into local minima that put the cord on the wrong hemisphere), so we use a deterministic PCA pre-rotation instead.

WORK   <- tempfile("banc_colormip_work_"); dir.create(WORK, recursive = TRUE)
LSM    <- "/path/to/20250306_C24-53_EN1-5-1060_female_vnc_40x-2.lsm"
TPL    <- "/path/to/JRC2018_VNC_FEMALE_461.nrrd"
py     <- reticulate::conda_python("r-reticulate")
pydir  <- system.file("python", package = "neuronbridger")

# (a) Split the LSM into per-channel NRRDs. For this acquisition
#     ch0 = GFP, ch1 = unused red, ch2 = NC82.
system2(py, c(file.path(pydir, "lsm_to_nrrd.py"), LSM, WORK,
              "--prefix", "sren_female",
              "--gfp-channel", 0L, "--nc82-channel", 2L))

# (b) NC82 tissue mask (Otsu + close + largest CC + small dilation).
system2(py, c(file.path(pydir, "lsm_build_nc82_mask.py"),
              file.path(WORK, "sren_female_nc82.nrrd"),
              file.path(WORK, "sren_female_nc82_mask.nrrd")))

# (c) PCA pre-rotate onto JRC2018VNCF axes, anchor GFP CoG at
#     (131, 508, 92) um -- this is the GFP CoG of the upstream KDRC
#     registered TIF, taken as a known-good rough placement of the SREN
#     arborisation in template coords.
#
#     PC1 (longest mask axis) -> template Y (anterior-posterior)
#     PC2 (medium)             -> template X (lateral)
#     PC3 (shortest)           -> template Z (dorso-ventral)
#     Sign of PC1 is set so the GFP CoG projects positively onto PC1
#     (i.e. lands at high template Y, the posterior tip). det(R)=+1.
dir.create(file.path(WORK, "rot"), recursive = TRUE)
system2(py, c(file.path(pydir, "lsm_pca_rotate.py"),
              file.path(WORK, "sren_female_nc82.nrrd"),
              file.path(WORK, "sren_female_gfp.nrrd"),
              file.path(WORK, "sren_female_nc82_mask.nrrd"),
              TPL,
              file.path(WORK, "tip_mask.nrrd"),      # ignored, see --target-um
              file.path(WORK, "rot"),
              "--align-source", "gfp_cog",
              "--target-um",    "\"131,508,92\""))

The script writes rot/nc82_rot.nrrd, rot/gfp_rot.nrrd, and rot/nc82_mask_rot.nrrd, all sharing the same physical origin and voxdims (0.4 µm XY / 0.7 µm Z) with identity Direction. The R wrapper in inst/scripts/banc_colormip_sren.R then resamples these onto the JRC2018VNCF grid via SimpleITK (one-shot linear resample), so the output reads cleanly as a template-space NRRD.

Step 2 — Mask GFP by the NC82 neuropil

A simple Otsu of the resampled NC82 gives a neuropil mask; we zero any GFP voxel that falls outside it. About half of the raw GFP voxels in this acquisition sit outside the neuropil — fluorescence bleed-through into cuticle, rectal muscle, and background — so this step is a big lever:

system2(py, c(file.path(pydir, "lsm_neuropil_mask.py"),
              "nc82_PCAonly_JRC2018VNCF.nrrd",    # rotated + resampled NC82
              "gfp_PCAonly_JRC2018VNCF.nrrd",     # rotated + resampled GFP
              "gfp_PCAonly_neuropilmasked_JRC2018VNCF.nrrd",
              "--mask-out", "neuropil_mask_JRC2018VNCF.nrrd"))
#> NC82 Otsu threshold: 40.04
#> neuropil mask voxels: 5,462,433
#> GFP after neuropil mask: nonzero 36,798  (49.9% of pre-mask)

The masked GFP NRRD on the JRC2018VNCF grid is what feeds into the rest of the pipeline.

Step 3 — Bridge JRCVNC2018F → JRC2018VNCU_HR 

ser_in_HR <- jrcvnc2018f_to_jrcvnc2018u_hr_h5(
  input  = "gfp_PCAonly_neuropilmasked_JRC2018VNCF.nrrd",
  output = file.path(WORK, "sren_female_in_JRC2018VNCU_HR.nrrd"))
# ~70 s on Apple silicon with 8 threads. Output: 573 x 1119 x 219
# voxels at 0.461 / 0.461 / 0.7 um.

The H5 (JRCVNC2018U_JRCVNC2018F.h5 from flybrains) stores its displacement field on the JRCVNC2018U grid, with dfield direction JRCVNC2018F → JRCVNC2018U (the file follows the brain <DEST>_<SRC>.h5 naming convention used by nat.jrcbrains). For F → U image resampling we want OUTPUT (U) → SOURCE (F) lookup — the inverse direction — so jrcvnc2018f_to_jrcvnc2018u_hr_h5() passes -i to RenderTransformed. Asking for the JRC2018VNCU_HR output grid directly works because JRC2018VNCU and JRC2018VNCU_HR share the same physical coordinate system; the JAR interpolates the dfield onto the requested grid in one shot.

Step 4 — Generate the query MIP 

query_png <- nrrd_to_mip(
  input        = ser_in_HR,
  savefolder   = WORK,
  method       = "direct",
  target_space = "VNC",
  threshold    = 0.80,
  denoise      = "median3d",
  format       = "png",
  save         = TRUE,
  overwrite    = TRUE)

threshold = 0.80 keeps the top 20 % of non-zero voxels by intensity in the bridged volume; the neuropil mask already removed most of the dim halo so this floor is just a small extra noise cut.

SREN query colour-MIP in JRC2018VNCU_HR (neuropil-masked GFP)
SREN query colour-MIP in JRC2018VNCU_HR (neuropil-masked GFP)

Step 5 — Build the BANC efferent MIP library 

LIB_DIR  <- file.path(WORK, "library", "mips")
dir.create(LIB_DIR, showWarnings = FALSE, recursive = TRUE)

# Shared root for every BANC GCS path used in this vignette.
GCS_ROOT <- "gs://lee-lab_brain-and-nerve-cord-fly-connectome"

# (a) BANC metadata — pull from the LIVE SeaTable `banc_meta` so we
# get the current `cell_type` + `manc_cell_type` bridge (both change
# with proofreading). The frozen `banc_888_meta.feather` on GCS is a
# snapshot; SeaTable is authoritative for annotation state and carries
# both the current `root_id` and the frozen `root_888` (which is what
# the cMIP filenames and precomputed layer object lists key on).
# Requires `BANCTABLE_TOKEN` in `.Renviron` — see `bancr::banctable_login`.
meta_live <- bancr::banctable_query(paste(
  "SELECT root_id, root_888, cell_type, manc_cell_type, cell_class,",
         "cell_sub_class, side, hemilineage, nerve, neuromere,",
         "neurotransmitter_predicted, peripheral_target_type,",
         "cell_function, flow",
  "FROM banc_meta"))
meta <- as.data.frame(meta_live) |>
  dplyr::filter(flow == "efferent") |>
  dplyr::select(root_888, side, hemilineage, nerve, neuromere,
                cell_class, cell_sub_class, cell_type, manc_cell_type,
                neurotransmitter_predicted, peripheral_target_type,
                cell_function)
nrow(meta)
#> [1] 1033

# (b) Match against the actual BANC color-MIP set (some efferents
# don't have a MIP rendered yet).
mips_root <- file.path(GCS_ROOT,
                       "neuron_colormips/template_alignment_240721",
                       "JRC2018_VNC_UNISEX_461") |> paste0("/")
all_mips  <- system2("gsutil", c("ls", mips_root), stdout = TRUE)
mip_ids   <- sub(".*/", "", sub("_in_JRC2018_VNC_UNISEX_461\\.png$", "", all_mips))
to_get    <- all_mips[mip_ids %in% meta$root_888]
length(to_get)
#> [1] 782

# (c) Parallel download (~2 MB total, takes <1 minute).
writeLines(to_get, file.path(WORK, "to_get.txt"))
system(sprintf("xargs -P 16 -I {} gsutil -q cp {} %s/ < %s",
               LIB_DIR, file.path(WORK, "to_get.txt")))
res <- colormip_search(
  query        = query_png,
  library      = LIB_DIR,
  threshold    = 100L,    # channel-sum brightness cutoff (signal floor)
  z_tolerance  = 8L,      # depth-LUT-index tolerance
  xy_shift     = 3L,      # +/- 3 px translation grid
  mirror       = FALSE,   # SREN is unilateral
  mc.cores     = 8L,
  verbose      = TRUE)
res$root_888 <- sub(".*/(\\d+)_in_JRC2018.*", "\\1", res$path)

head(res[, c("root_888", "score", "n_match", "dx", "dy", "mirror")], 6)
#>             root_888     score n_match dx dy mirror
#> 1 720575941545785276 0.2122492     201  3 -3  FALSE
#> 2 720575941661415804 0.1774023     168  3 -3  FALSE
#> 3 720575941549868093 0.1636748     155  3 -3  FALSE
#> 4 720575941513645891 0.1499472     142  3 -3  FALSE
#> 5 720575941686473228 0.1488912     141  3 -3  FALSE
#> 6 720575941494055038 0.1478353     140  3 -3  FALSE

Neuropil-masking the query pushed the top cMIP score from ~0.072 (without the mask) to 0.21 here — three times more pixel matches because the noise floor that previously dominated the MIP is gone.

Step 7 — Cross-check with NBLAST fwd+rev mean (full elastix bridge)

We score the SREN dotprops against the full ~1,033-cell BANC efferent library using nat.nblast::nblast(..., normalised = TRUE) both directions and averaging the two — the standard NBLAST scorer. Points are bridged from JRC2018VNCF µm into BANC µm via bancr::banc_to_JRC2018F with method = "elastix" (the full manual-affine + elastix-affine + coarse-B-spline + fine-B-spline chain shipped at gs://lee-lab_brain-and-nerve-cord-fly-connectome/registrations/vnc_240721/), which preserves the bilateral SREN signal at the tip. Earlier revisions used bancr::jrcvnc2018f_to_banc_tpsreg (a TPS approximation of the same registration) and reported forward-only NBLAST because the TPS placed the bridged signal on one side of the cord — that shortcut is no longer needed.

library(nat.nblast)

# (a) Intensity-weighted SREN dotprops in BANC microns. Use ALL non-zero
# neuropil-masked voxels (no median filter, no largest-CC selection) so
# the bilateral pattern in the LSM survives.
ser <- nat::read.nrrd("gfp_PCAonly_neuropilmasked_JRC2018VNCF.nrrd")
storage.mode(ser) <- "integer"
idx <- which(ser > 0, arr.ind = TRUE)
ser_full <- data.frame(
  X = (idx[,1] - 1L) * 0.461122 + 0.461122/2,
  Y = (idx[,2] - 1L) * 0.461122 + 0.461122/2,
  Z = (idx[,3] - 1L) * 0.7      + 0.35,
  I = as.integer(ser[ser > 0]))

set.seed(11L); N <- 50000L
keep <- sample.int(nrow(ser_full), min(N, nrow(ser_full)),
                   prob = ser_full$I / sum(ser_full$I))
pts_F <- as.matrix(ser_full[keep, c("X","Y","Z")])
# Full elastix JRC2018F -> BANC (points in um).
pts_BANC_um <- bancr::banc_to_JRC2018F(
  pts_F, region = "vnc", banc.units = "um",
  inverse = TRUE, method = "elastix")
ser_dp <- nat::dotprops(pts_BANC_um, k = 5L)

# (b) Full-library NBLAST (~1,033 BANC efferents). The L2 dotprops are
# pre-cached locally as an .rds: fetch each cell's L2 skeleton with
# `bancr::banc_read_l2dp(meta$root_888)` once, drop NULLs/duplicates,
# and `saveRDS()` the resulting `neuronlist` so this step is offline.
banc_dps <- readRDS("banc_l2dp_efferent_full.rds")
banc_dps <- banc_dps[!sapply(banc_dps, is.null)]
banc_dps <- banc_dps[!duplicated(names(banc_dps))]

fwd <- nat.nblast::nblast(ser_dp, nat::as.neuronlist(banc_dps),
                          smat = nat.nblast::smat.fcwb, normalised = TRUE)
rev <- nat.nblast::nblast(nat::as.neuronlist(banc_dps), ser_dp,
                          smat = nat.nblast::smat.fcwb, normalised = TRUE)
nb <- data.frame(root_888    = names(fwd),
                 nblast_mean = (as.numeric(fwd) + as.numeric(rev)) / 2)
nb <- merge(nb, meta, by = "root_888")
nb <- nb[order(-nb$nblast_mean), ]
head(nb[, c("root_888", "nblast_mean", "cell_type", "manc_cell_type",
            "cell_sub_class", "side", "neurotransmitter_predicted")], 6)

Under this pipeline, the top-6 NBLAST hits are dominated by BANC cell_type = EFFabg40 and its close relatives (EFFabg43, EFFabg45), all sitting inside cell_sub_class = "abdomen_neurosecretory_cell" or "abd_Dsx_OA". These are doublesex-expressing sex-dimorphic abdominal neurosecretory cells, largely octopaminergic — a much better biological match for “sex-peptide-receptor rectal enteric neuron” than the motor-neuron interpretation an earlier revision of this vignette proposed. The MANC bridge (manc_cell_type) resolves these to legacy MANC names like MNad17, MNad48, MNad04, MNad69.

Top-6 BANC efferent hits ranked by NBLAST fwd+rev mean (full-elastix bridge)
Top-6 BANC efferent hits ranked by NBLAST fwd+rev mean (full-elastix bridge)

Step 8 — Side-by-side: SREN query vs top BANC cMIP hit

SREN LM (left) vs top BANC cMIP hit (right)
SREN LM (left) vs top BANC cMIP hit (right)

Step 8b — The hypothesised SREN pair: manc_cell_type EN00B016

Neither the cMIP top nor the NBLAST top puts specifically the canonical SPR pair at rank 1, but both consistently rank a bilateral pair with manc_cell_type = "EN00B016" (exactly 2 cells, one per side in BANC) in the top ~10 % of the library. These are the biologically motivated SREN candidates:

  • left — root_888 720575941680133053, BANC cell_type = EFFabg09, MANC EN00B016, abdomen_motor_neuron, ACh (hemilineage 06A);
  • right — root_888 720575941659397456, BANC cell_type = EN00B016, MANC EN00B016, ventral_nerve_cord_neurosecretory_cell / abdomen_neurosecretory_cell, octopamine (hemilineage 00B).

They meet the bilateral-pair expectation for a sex-peptide-receptor population and land in the abd_Dsx_OA family the search prefers. Their MANC bridge to a single EN00B016 label suggests MANC groups them as one cell type across sides even though BANC currently annotates them as distinct cell_type names.

Hypothesised SREN pair (BANC manc_cell_type EN00B016)
Hypothesised SREN pair (BANC manc_cell_type EN00B016)

What we recovered

Under PCA pre-rotation + neuropil mask + full-elastix JRC2018F→BANC bridge + NBLAST fwd+rev mean, the top-6 BANC efferents are all in the abd_Dsx_OA / abdomen-neurosecretory family (cell_class = ventral_nerve_cord_neurosecretory_cell), spread across a handful of BANC EFFabg* cell_types and their MANC bridges:

rank BANC cell_type MANC manc_cell_type side NT cell_sub_class
1 EFFabg40 MNad17 right octopamine abdomen_neurosecretory
2 EFFabg40 MNad17 right octopamine abdomen_neurosecretory
3 EFFabg43 MNad48 right octopamine abd_Dsx_OA
4 EFFabg39 MNad04 left octopamine abd_Dsx_OA
5 EFFabg40 MNad17 right dopamine abdomen_neurosecretory
6 EFFabg40 MNad48 left — abdomen_neurosecretory

EFFabg40 (7 cells total in v888) accounts for most of the top hits; its MANC bridge is MNad17. The precise identity is most likely a member of the abd_Dsx_OA / abdomen-neurosecretory family; the best specific candidate for the SREN bilateral pair is manc_cell_type = EN00B016 (2 cells, one per side). Both L and R members appear in the top ~10 % of the ranking.

Step 9 — Open each match in Spelunker

Four Neuroglancer precomputed layers ship the SREN LM signal in BANC voxel space, corresponding to successive refinements of the JRC2018F→BANC bridge:

GCS path registration voxel size
light_level/kdrc/SREN_female_aligned240721_to_BANC.ng bancr TPS (npmasked GFP) 800 nm
light_level/kdrc/SREN_female_aligned240721_to_BANC_elastix.ng bancr TPS + per-specimen elastix 800 nm
light_level/kdrc/SREN_female_aligned240721_to_BANC_elastix_400nm.ng bancr TPS + per-specimen elastix 400 nm
light_level/kdrc/SREN_female_aligned240721_to_BANC_fullElastix_400nm.ng full elastix chain (recommended) 400 nm

Prefer ..._fullElastix_400nm.ng — that’s the registration-correct version, bilateral signal preserved, matching the NBLAST bridge used in Step 7.

LM_GS <- paste0("precomputed://", GCS_ROOT,
                "/light_level/kdrc/",
                "SREN_female_aligned240721_to_BANC_fullElastix_400nm.ng")
BASE  <- paste0("https://spelunker.cave-explorer.org/#!middleauth+",
                "https://global.daf-apis.com/nglstate/api/v1/",
                "6450802162925568")

top <- head(nb, 6)
top$ngl_url <- vapply(top$root_888, function(rid)
  bancr::banc_lm_scene(
    lm_url     = LM_GS,
    layer_name = "KDRC SREN (female, full elastix, 400 nm)",
    range      = c(1, 60),
    opacity    = 0.55,
    blend      = "additive",
    ids        = as.character(rid),
    url        = BASE,
    shorten    = TRUE),
  character(1))

Caveats

  • Anchor placement. The PCA rotation needs a target placement in template coords; we use the GFP CoG of an upstream KDRC-registered TIF (131, 508, 92 µm) as the anchor. If you don’t have that reference, use the fixed-mask CoG fallback in lsm_pca_rotate.py (default --align-source nc82_mask against a Y > 350 µm posterior tip mask) and accept a few µm of placement drift.
  • Sign disambiguation. The PCA orientation has a residual L/R ambiguity (PC2 sign). The default --pc2-sign +1 works for this acquisition; if your data lands on the wrong hemisphere, re-run with --pc2-sign -1.
  • Bridge accuracy. The Saalfeldlab JRC VNC H5 was generated from population averages and is small in the body of the VNC but can be imprecise at the periphery (where SREN axons leave). For sub-voxel precision, register your specific NC82 channel onto JRC2018VNCU_HR via Elastix multi-resolution affine + B-spline (the same recipe as lm_to_jrc2018u_elastix for the brain).
  • Library coverage. Only ~75 % of flow == "efferent" BANC v888 cells (782 / 1035) had a colour-MIP rendered at the time of writing. Re-run Step 5 as more get rendered.
  • Forward-only NBLAST. This is the right scorer when the LM query has only a small in-cord arbor and the targets have long extra-cord axons. For symmetric queries (e.g. a complete neuron skeleton), use the mean of forward + reverse instead.
  • Mirror. We run cMIP and NBLAST with mirror = FALSE since SREN is unilateral. The contralateral twin of the top hit sits at NBLAST rank 88 — visible only with a mirror-aware secondary scoring pass.

Citations

  • SREN identification (this query): Chae HS, Subay OH, Kim DH, Yoon SE, Kim YJ. Rectal Enteric Neurons Optimize Fecal Production in Response to Mating Signals. Conference talk, APDNC4, 2026. Department of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea (corresponding author: Young-Joon Kim, ; Korea Drosophila Resource Center, GIST). Identifies a pair of sex-peptide-receptor neurons at the abdominal-ganglion tip whose efferents innervate the rectum and gate ROD production in response to mating; downstream targets ascend to the brain via Trans-Tango.
  • BANC connectome: Bates et al. (2025). The Brain-And-Nerve-Cord fly Connectome (BANC): a complete adult female Drosophila central nervous system at synapse resolution. bioRxiv. https://www.biorxiv.org/content/10.1101/2025.07.31.667571v1
  • NeuronBridge / ColorMIP: Otsuna, Ito, Kawase. (2018). Color depth MIP mask search: a new tool to expedite Split-GAL4 creation. bioRxiv. https://doi.org/10.1101/318006
  • NBLAST: Costa, Manton, Ostrovsky, Prohaska, Jefferis. (2016). NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases. Neuron. https://doi.org/10.1016/j.neuron.2016.06.012
  • JRC2018 VNC templates: Bogovic et al. (2020). An unbiased template of the Drosophila brain and ventral nerve cord. PLOS ONE. https://doi.org/10.1371/journal.pone.0236495
  • flybrains Python package (Saalfeldlab JRC VNC H5 distribution): Schlegel et al., navis-org/navis-flybrains.