{
"cells": [
{
"cell_type": "markdown",
"id": "a11493c9",
"metadata": {},
"source": [
"# SCALLOPS ARV-471\n",
"\n",
"{func}`~mantispy.ds.scallops_arv471` serves the drug arm of a genome-scale optical pooled CRISPR screen from `Genentech/scallops-manuscript`. Cells carry a guide library and are treated with ARV-471 (vepdegestrant), a PROTAC that recruits the CRL4-CRBN E3 ligase to the estrogen receptor and drives its degradation. In-situ sequencing reads each cell's guide, and a phenotype round stains DNA and the estrogen receptor and counts ESR1, CCND1 and GREB1 transcripts. It is a single-cell screen: the base is over two million cells, each with nine phenotype features. Those nine hand-picked readouts are the targeted-readout counterpart to the broad, roughly 1,278-feature morphology of {func}`~mantispy.ds.cp_posh`.\n",
"\n",
"A guide that knocks out a gene the drug needs to work rescues the receptor, so its cells keep the estrogen-receptor level of an untreated cell. The genes with that known mechanism are the members of the hijacked ligase, `CRBN`, `DDB1`, `CUL4A` and `CUL4B`, and `ESR1` itself.\n",
"\n",
"**Use it if** your own data is a single-cell optical pooled screen, especially one with a small targeted phenotype readout rather than a broad morphology profile. No tutorial is built on it; aggregate cells to guides as [CRISPR knockouts](../case_studies/crispr.ipynb) does before scoring."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "14bc5225",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T01:00:37.071266Z",
"iopub.status.busy": "2026-10-21T01:00:37.070872Z",
"iopub.status.idle": "2026-10-21T01:00:40.609166Z",
"shell.execute_reply": "2026-10-21T01:00:40.607521Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"\n",
"import mantispy as mt"
]
},
{
"cell_type": "markdown",
"id": "e440716c",
"metadata": {},
"source": [
"This page uses the guide-level aggregate, one median profile per guide, so it stays small."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2b93bf5c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T01:00:40.613048Z",
"iopub.status.busy": "2026-10-21T01:00:40.612805Z",
"iopub.status.idle": "2026-10-21T01:00:40.772760Z",
"shell.execute_reply": "2026-10-21T01:00:40.771842Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 680 × 9\n",
" obs: 'Metadata_Gene', 'Metadata_sgRNA', 'Metadata_CellCount', 'Metadata_Control_Type', 'Metadata_Control', 'Metadata_Perturbation', 'Metadata_Well', 'Metadata_Row', 'Metadata_Col', 'Metadata_ObjectType', 'Metadata_Perturbation_Type'\n",
" var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind'\n",
" uns: 'mantispy'\n",
" layers: None (.X)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adata = mt.ds.scallops_arv471(aggregated=True)\n",
"adata"
]
},
{
"cell_type": "markdown",
"id": "cc7b506c",
"metadata": {},
"source": [
"## The rescue reads out in the estrogen receptor\n",
"\n",
"Grouping the per-guide estrogen-receptor intensity by gene puts the ligase members and `ESR1` beside the non-targeting guides. Knocking out `CRBN` or `DDB1` keeps the receptor high, because the drug can no longer assemble the ligase that degrades it; guides against `ESR1` sit at the bottom, since there is no receptor left to stain."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4cfbd86b",
"metadata": {
"execution": {
"iopub.execute_input": "2026-10-21T01:00:40.777037Z",
"iopub.status.busy": "2026-10-21T01:00:40.776887Z",
"iopub.status.idle": "2026-10-21T01:00:41.116503Z",
"shell.execute_reply": "2026-10-21T01:00:41.115374Z"
}
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"genes = [\"CRBN\", \"DDB1\", \"CUL4A\", \"CUL4B\", \"ESR1\", \"nontargeting\"]\n",
"selected = adata[adata.obs[\"Metadata_Gene\"].isin(genes)]\n",
"er = np.asarray(selected[:, \"Nuclei_Intensity_MedianIntensity_ER\"].X).ravel()\n",
"df = pd.DataFrame({\"gene\": selected.obs[\"Metadata_Gene\"].astype(str).to_numpy(), \"ER intensity\": er})\n",
"fig = px.box(\n",
" df,\n",
" x=\"gene\",\n",
" y=\"ER intensity\",\n",
" points=\"all\",\n",
" category_orders={\"gene\": genes},\n",
" title=\"Estrogen-receptor intensity per guide\",\n",
")\n",
"fig.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}