An Open-Source Pipeline for Calcium Imaging and All-Optical Physiology in Human Stem Cell-Derived Neurons.

Code ↔ Paper

19 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 19 matches

[1] § Experimental Section/Methods › Evoked Activity Experiments ↔ src/cali/sqlmodel/_util.py, lines 544–665 · score 0.87 · LED power, pulse duration, stimulation events, optogenetic stimulation, Detected peaks, Peak amplitudes
[2] § Experimental Section/Methods › Peak Detection ↔ src/cali/gui/_analysis_gui.py, lines 790–849 · score 0.82 · minimum peak height, find peaks, F0 traces, calcium transients, scipy, noise
[3] § Experimental Section/Methods › Evoked Activity Experiments ↔ src/cali/sqlmodel/_visualize_experiment.py, lines 260–338 · score 0.78 · stimulation parameters, LED power, evoked activity, pulse duration, stimulation mask, frame
[4] § Results › Quantification of Optically Evoked Activity ↔ src/cali/plot/_single_wells_plots/correlation/_plot_evoked_correlation_synchrony.py, lines 1097–1182 · score 0.76 · Stim median, Pairwise Pearson Correlation, Global median, Stim Periods, Stim Windows, stimulated ROIs
[5] § Experimental Section/Methods › Multi‐Dimensional Acquisition (MDA) Interface ↔ src/micromanager_gui/_widgets/_mm_console.py, lines 49–144 · score 0.75 · micromanager gui, pymmcore plus, Python, useq, MDA, widgets
[6] § Experimental Section/Methods › Peak Detection ↔ src/cali/sqlmodel/_util.py, lines 544–665 · score 0.74 · OASIS package, prominence threshold, peak height, inter, dynamically, denoised
[7] § Results › Modular Acquisition Platform for Calcium Imaging and Optogenetics ↔ src/micromanager_gui/_widgets/_mm_console.py, lines 49–144 · score 0.69 · micromanager gui, pymmcore plus, Python, useq, widgets
[8] § Experimental Section/Methods › Features Extraction ↔ src/cali/analysis/_analysis_runner.py, lines 263–407 · score 0.68 · inter event interval, mask area, peak amplitude, IEI, active, trace
[9] § Results › Quantification of Optically Evoked Activity ↔ src/cali/plot/_single_wells_plots/correlation/_plot_evoked_correlation_synchrony.py, lines 552–685 · score 0.67 · pairwise Pearson correlation, global median, stimulated neurons, synchrony, evoked, stimulus
[10] § Experimental Section/Methods › Statistical Independence and Experimental Replicates ↔ src/cali/plot/_multi_wells_plots/_util.py, lines 391–488 · score 0.62 · biological replicate, technical replicates, hierarchical, nested, ROI, FOVs
[11] § Experimental Section/Methods › Multi‐Dimensional Acquisition (MDA) Interface ↔ src/micromanager_gui/_widgets/_viewers/_mda_viewer/_data_wrappers.py, lines 19–68 · score 0.59 · micromanager gui, pymmcore plus, MDA, widgets
[12] § Results › Integrated Data Exploration and Analysis via Cali ↔ src/cali/sqlmodel/_model.py, lines 751–803 · score 0.58 · custom Cellpose, custom model, Quality, Cyto3, segmentation, Cali
[13] § Experimental Section/Methods › Multi‐Dimensional Acquisition (MDA) Interface ↔ src/micromanager_gui/_engine.py, lines 66–126 · score 0.56 · hardware autofocus devices, MDA, channel, position, GUI
[14] § Results › Modular Acquisition Platform for Calcium Imaging and Optogenetics ↔ src/micromanager_gui/_widgets/_viewers/_mda_viewer/_data_wrappers.py, lines 19–68 · score 0.54 · micromanager gui, pymmcore plus, platform, widgets
[15] § Experimental Section/Methods › Quantitative Evaluation of Segmentation With a Custom Trained Cellpose Model ↔ src/micromanager_gui/_realtime_cellpose_segmentation.py, lines 131–197 · score 0.53 · Cellpose model, pretrained, Cyto3, Segmentation
[16] § Experimental Section/Methods › Quantitative Evaluation of Segmentation With a Custom Trained Cellpose Model ↔ src/cali/sqlmodel/_model.py, lines 751–803 · score 0.52 · Cellpose model, Quality, Cyto3, detections, Segmentation
[17] § Results › Integrated Data Exploration and Analysis via Cali ↔ src/cali/analysis/_fov_metrics.py, lines 1440–1562 · score 0.52 · Pearson correlation matrices, calcium traces, metrics, row, synchrony, denoised
[18] § Experimental Section/Methods › Data Analysis ↔ src/cali/gui/_cali_gui.py, lines 1967–2096 · score 0.51 · cali GUI, plate maps, genotypes, treatment
[19] § Experimental Section/Methods › Compute ΔF/F0 ↔ src/cali/gui/_extraction_gui.py, lines 395–527 · score 0.51 · sliding window, fluorescence F0, baseline, timepoint, traces

Paper

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The authors' code

Python · 666 lines · 24 KB · BSD-3-Clause · 2 matches

"""Utility functions for cali.sqlmodel database operations.
This module provides helper functions for database operations including:
- Creating database tables
- Loading experiments from database
- Checking analysis settings consistency
"""
from __future__ import annotations
from dataclasses import dataclass, replace
from pathlib import Path
from typing import TYPE_CHECKING, Any, TypeVar
from sqlalchemy import text
from sqlalchemy.exc import IntegrityError
from sqlmodel import Session, create_engine, select
from cali._constants import DEFAULT_CALI_DB_NAME
from ._model import Experiment
if TYPE_CHECKING:
from sqlalchemy.engine import Engine
from cali.logger import cali_logger
def migrate_analysis_settings(engine: Engine) -> None:
"""Add missing columns to analysis_settings table for existing databases.
This is safe to call multiple times — it only adds columns that don't exist.
"""
with engine.connect() as conn:
existing_cols = {
row[1] for row in conn.execute(text("PRAGMA table_info(analysis_settings)"))
}
if not existing_cols:
return # table doesn't exist yet
if "enable_calcium" not in existing_cols:
conn.execute(
text(
"ALTER TABLE analysis_settings "
"ADD COLUMN enable_calcium BOOLEAN DEFAULT 1 NOT NULL"
)
)
if "enable_spikes" not in existing_cols:
conn.execute(
text(
"ALTER TABLE analysis_settings "
"ADD COLUMN enable_spikes BOOLEAN DEFAULT 1 NOT NULL"
)
)
conn.commit()
def create_database_and_tables(engine: Engine) -> None:
"""Create all database tables.
Parameters
----------
engine : sqlalchemy.engine.Engine
Database engine
Example
-------
>>> from sqlmodel import create_engine
>>> from cali.sqlmodel import create_database_and_tables
>>> engine = create_engine("sqlite:///calcium_analysis.db")
>>> create_database_and_tables(engine)
"""
from sqlmodel import SQLModel
# Import all models to register them with SQLModel metadata
from ._model import ( # noqa: F401
FOV,
ROI,
AnalysisSettings,
CaliResult,
Condition,
DataAnalysis,
DetectionSettings,
ExtractionSettings,
FOVAnalysis,
Mask,
Plate,
Traces,
Well,
WellCondition,
)
SQLModel.metadata.create_all(engine)
migrate_analysis_settings(engine)
def save_experiment_to_database(
experiment: Experiment,
output_path: Path | str,
*,
database_name: str = DEFAULT_CALI_DB_NAME,
overwrite: bool = False,
echo: bool = False,
) -> None:
"""Save an experiment object tree to a SQLite database.
This function saves the experiment and returns nothing, following SQLModel
best practices of not returning objects to discourage keeping large object
trees in memory. Load the experiment fresh from the database when needed
using load_experiment_from_database().
Parameters
----------
experiment : Experiment
Experiment object
output_path : Path | str
Output directory to save the database file.
database_name : str, optional
Name of the database file (e.g., "cali.db"). Defaults to "results.cali".
overwrite : bool, optional
Whether to overwrite existing database file, by default False
echo : bool, optional
Whether to enable SQLAlchemy engine echo for debugging, by default False
Example
-------
>>> from pathlib import Path
>>> save_experiment_to_database(exp, overwrite=True)
>>> # Later, load fresh from DB when needed:
>>> db_path = Path(exp.output_path) / exp.database_name
>>> exp = load_experiment_from_database(db_path)
"""
# Determine database path
db_name = database_name if database_name is not None else DEFAULT_CALI_DB_NAME
assert db_name is not None # Guaranteed by the check above
if not db_name.endswith(".cali"):
db_name += ".cali"
db_path = Path(output_path) / db_name
# Ensure parent directory exists
db_path.parent.mkdir(parents=True, exist_ok=True)
if overwrite and db_path.exists():
db_path.unlink()
engine = create_engine(
f"sqlite:///{db_path}",
echo=echo,
connect_args={"timeout": 30.0, "check_same_thread": False},
pool_pre_ping=True,
)
create_database_and_tables(engine)
try:
with Session(engine) as session:
# Pre-resolve conditions BEFORE merge to avoid session.merge()
# limitations with link_model many-to-many relationships.
# Safely check if plate is loaded without triggering lazy load
# on detached instance
from sqlalchemy import inspect as sa_inspect
from sqlalchemy import or_
from cali.sqlmodel._model import Condition
insp = sa_inspect(experiment)
plate_loaded = "plate" in insp.dict and insp.dict["plate"] is not None
# Store original well-to-conditions mapping BEFORE merge
well_condition_map: dict[int, list[tuple[str, str]]] = {}
if plate_loaded and experiment.plate is not None:
# Collect all unique (name, condition_type) pairs from all wells
conditions_needed: set[tuple[str, str]] = set()
for idx, well in enumerate(experiment.plate.wells):
# Store this well's condition keys
well_keys = [(c.name, c.condition_type) for c in well.conditions]
well_condition_map[idx] = well_keys
conditions_needed.update(well_keys)
# Batch fetch existing conditions in ONE query
condition_lookup: dict[tuple[str, str], Condition] = {}
if conditions_needed:
or_clauses = [
(Condition.name == name) & (Condition.condition_type == ctype)
for name, ctype in conditions_needed
]
existing = session.exec(
select(Condition).where(or_(*or_clauses))
).all()
condition_lookup = {(c.name, c.condition_type): c for c in existing}
# Merge experiment into session first
merged_exp = session.merge(experiment)
# Now fix up conditions on the session-attached wells
if plate_loaded and merged_exp.plate is not None:
for idx, well in enumerate(merged_exp.plate.wells):
# Use original well's condition keys (before merge)
condition_keys = well_condition_map.get(idx, [])
if not condition_keys:
# No conditions for this well, skip it
continue
resolved_conditions: list[Condition] = []
for key in condition_keys:
existing_cond = condition_lookup.get(key)
if existing_cond:
# Use existing condition from DB
resolved_conditions.append(existing_cond)
else:
# Condition doesn't exist - query for it
name, ctype = key
stmt = select(Condition).where(
(Condition.name == name)
& (Condition.condition_type == ctype)
)
cond_in_session = session.exec(stmt).first()
if cond_in_session:
resolved_conditions.append(cond_in_session)
# else: condition not found, skip it
# Assign resolved conditions (replaces whatever merge() set)
if resolved_conditions:
well.conditions = resolved_conditions
session.commit()
# Refresh to get the ID assigned by the database
session.refresh(merged_exp)
# Update the original experiment object with the database ID
experiment.id = merged_exp.id
cali_logger.info(
f"💾 Experiment analysis updated and saved to database at {db_path}."
)
except IntegrityError as e:
cali_logger.error(
f"❌ Failed to save experiment to database. "
f"Integrity constraint violated: {e}"
)
raise
finally:
# Dispose engine to release database connections (Windows compatibility)
engine.dispose(close=True)
def load_experiment_from_database(
db_path: Path | str,
experiment_name: str | None = None,
echo: bool = False,
) -> Experiment | None:
"""Load an experiment from SQLite database with all relationships.
This function loads a complete experiment snapshot for read-only analysis
or display. The returned object is detached from the session (expunged) and
can be used outside the session context.
Parameters
----------
db_path : Path | str
Path to SQLite database file
experiment_name : str | None, optional
Name of specific experiment to load. If None, loads the first experiment.
echo : bool, optional
Whether to enable SQLAlchemy engine echo for debugging, by default False
Returns
-------
Experiment | None
Loaded experiment with all relationships, or None if not found.
The object is detached (expunged) and can be used outside the session.
Example
-------
>>> from pathlib import Path
>>> # For read-only display/analysis:
>>> exp = load_experiment_from_database("analysis.db", "my_experiment")
>>> if exp:
... print(f"Loaded {len(exp.plate.wells)} wells")
>>>
>>> # For modifications, use engine + ID pattern instead:
>>> engine = create_engine("sqlite:///analysis.db")
>>> with Session(engine) as session:
... exp = session.get(Experiment, experiment_id)
... exp.name = "Updated Name" # Modify within session
... session.commit() # Save changes
"""
from pathlib import Path
from sqlalchemy.exc import OperationalError
from sqlmodel import select
# Check if database file exists
db_path = Path(db_path) if isinstance(db_path, str) else db_path
if not db_path.exists():
return None
# Convert to string for consistency
db_path_str = str(db_path)
engine = create_engine(
f"sqlite:///{db_path_str}",
echo=echo,
connect_args={"timeout": 30.0, "check_same_thread": False},
pool_pre_ping=True,
)
try:
# Use context manager to ensure session is properly closed
with Session(engine, expire_on_commit=False) as session:
# Query for experiment
if experiment_name:
statement = select(Experiment).where(Experiment.name == experiment_name)
else:
statement = select(Experiment)
try:
experiment = session.exec(statement).first()
except OperationalError:
# Database exists but tables don't (corrupted or empty database)
return None
if not experiment:
return None
# Force load all relationships to prevent DetachedInstanceError
_force_load_experiment_relationships(experiment)
# Make the instance independent of the session
session.expunge(experiment)
# Session automatically closed here
return experiment # type: ignore
finally:
# Dispose engine to release database connections (Windows compatibility)
engine.dispose(close=True)
def _force_load_experiment_relationships(experiment: Experiment) -> None:
"""Force load all experiment relationships to prevent DetachedInstanceError.
This function eagerly loads all relationships on an experiment object while
the session is still active, ensuring the object can be used outside the session.
Parameters
----------
experiment : Experiment
The experiment object to load relationships for
"""
# Force load ALL relationships deeply while session is still open
# This prevents DetachedInstanceError when accessed later
if experiment.plate:
_ = len(experiment.plate.wells) # Force load wells
for well in experiment.plate.wells:
_ = len(well.conditions) # Force load conditions
_ = len(well.fovs) # Force load fovs
for fov in well.fovs:
_ = len(fov.rois) # Force load rois
for roi in fov.rois:
# Force load all ROI relationships
_ = len(roi.traces_history)
_ = len(roi.data_analysis_history)
_ = roi.roi_mask
def has_fov_analysis(db_path: str | Path, fov_name: str) -> bool:
"""Check if a specific FOV has been analyzed by querying database directly.
Directly queries the database to check if the FOV exists and has analyzed ROIs.
Parameters
----------
db_path : str | Path
Path to the SQLite database file
fov_name : str
Name of the FOV to check (e.g., "B5_0000")
Returns
-------
bool
True if the FOV exists and has analyzed ROIs, False otherwise
Example
-------
>>> from cali.sqlmodel import has_fov_analysis
>>> if has_fov_analysis("analysis.db", "B5_0000"):
... print("B5_0000 has been analyzed")
"""
from sqlmodel import select
from ._model import FOV, ROI, Traces
engine = create_engine(
f"sqlite:///{db_path}",
connect_args={"timeout": 30.0, "check_same_thread": False},
pool_pre_ping=True,
)
try:
with Session(engine) as session:
# Check if this specific FOV has any ROIs with Traces entries
# (which indicates the FOV has been analyzed)
statement = (
select(Traces).join(ROI).join(FOV).where(FOV.name == fov_name).limit(1)
)
result = session.exec(statement).first()
return result is not None
finally:
engine.dispose(close=True)
def has_experiment_analysis(db_path: str | Path) -> bool:
"""Check if experiment has any analyzed data by querying database directly.
Directly queries the database to check if any ROIs exist with analysis data.
Parameters
----------
db_path : str | Path
Path to the SQLite database file
Returns
-------
bool
True if any ROIs have analysis data, False otherwise
Example
-------
>>> from cali.sqlmodel import has_experiment_analysis
>>> if has_experiment_analysis("analysis.db"):
... print("Experiment has analysis data")
"""
from sqlmodel import select
from ._model import Traces
engine = create_engine(
f"sqlite:///{db_path}",
connect_args={"timeout": 30.0, "check_same_thread": False},
pool_pre_ping=True,
)
try:
with Session(engine) as session:
# Check if any Traces entries exist (indicates analysis has been run)
statement = select(Traces).limit(1)
result = session.exec(statement).first()
return result is not None
finally:
engine.dispose(close=True)
def _parse_well_name(well_name: str) -> tuple[int, int]:
"""Parse well name like 'B5' or 'AE19' into (row, column) indices.
Supports both single-letter (A-Z) and multi-letter (AA, AB, ...) row names
for plates with more than 26 rows.
Parameters
----------
well_name : str
Well name (e.g., 'B5', 'A1', 'AE19')
Returns
-------
tuple[int, int]
(row, column) - Zero-indexed row and column
Raises
------
ValueError
If well_name is not in the expected format
"""
if not well_name or len(well_name) < 2:
raise ValueError(
f"Invalid well name: '{well_name}'. Expected format like 'B5', 'AE19'"
)
# Split into letter prefix and number suffix
i = 0
while i < len(well_name) and well_name[i].isalpha():
i += 1
if i == 0:
raise ValueError(f"Invalid well name: '{well_name}'. Must start with letter(s)")
if i == len(well_name) or not well_name[i:].isdigit():
raise ValueError(
f"Invalid well name: '{well_name}'. Expected format like 'B5', 'AE19' "
f"(letter(s) followed by number)"
)
row_label = well_name[:i]
row = _label_to_row_index(row_label)
col = int(well_name[i:]) - 1
return row, col
def _label_to_row_index(label: str) -> int:
"""Convert well row label to zero-indexed row number.
Supports single and multi-letter labels using base-26 alphabet.
A=0, B=1, ..., Z=25, AA=26, AB=27, ..., AZ=51, etc.
Parameters
----------
label : str
Row label (e.g., 'A', 'Z', 'AA', 'AE')
Returns
-------
int
Zero-indexed row number
Examples
--------
>>> _label_to_row_index("A")
0
>>> _label_to_row_index("Z")
25
>>> _label_to_row_index("AA")
26
>>> _label_to_row_index("AE")
30
"""
label = label.upper()
result = 0
for char in label:
result = result * 26 + (ord(char) - ord("A") + 1)
return result - 1
# OLD WAY TO STORE DATA --------------------------------------------------------------
# Define a type variable for the BaseClass
T = TypeVar("T", bound="BaseClass")
@dataclass
class BaseClass:
"""Base class for all classes in the package."""
def replace(self: T, **kwargs: Any) -> T:
"""Replace the values of the dataclass with the given keyword arguments."""
return replace(self, **kwargs)
# fmt: off
@dataclass
class ROIData(BaseClass):
"""Data container for ROI (Region of Interest) analysis results.
This dataclass stores comprehensive analysis data for a single ROI including
raw fluorescence traces, neuropil correction, calcium dynamics (dff, denoised),
peak detection, inferred spikes, and experimental metadata.
Parameters
----------
well_fov_position : str
Position identifier (e.g., "B5_0000_p0" for well B5, fov0, position 0)
raw_trace : list[float] | None
Original raw fluorescence trace before any neuropil correction
corrected_trace : list[float] | None
Raw fluorescence trace after neuropil correction (if enabled),
otherwise same as raw_trace. This is used for all
downstream analysis.
neuropil_trace : list[float] | None
Fluorescence trace from the neuropil (donut-shaped region around ROI)
neuropil_correction_factor : float | None
Correction factor used for neuropil subtraction
dff : list[float] | None
ΔF/F (delta F over F) - normalized fluorescence change
den_dff : list[float] | None
Denoised ΔF/F trace (using OASIS algorithm) for calcium event detection
peaks_den_dff : list[float] | None
Indices of detected peaks in the denoised trace
peaks_amplitudes_den_dff : list[float] | None
Amplitude values of detected peaks in denoised trace
peaks_prominence_den_dff : float | None
Prominence threshold used for peak detection
peaks_height_den_dff : float | None
Height threshold used for peak detection
inferred_spikes : list[float] | None
Inferred spike probabilities from deconvolution
inferred_spikes_threshold : float | None
Threshold for spike detection
den_dff_frequency : float | None
Frequency of calcium events in Hz
condition_1 : str | None
First experimental condition (e.g., genotype)
condition_2 : str | None
Second experimental condition (e.g., treatment)
cell_size : float | None
ROI area in µm² or pixels
cell_size_units : str | None
Units for cell_size ("µm" or "pixel")
elapsed_time_list_ms : list[float] | None
Timestamp for each frame in milliseconds
total_recording_time_sec : float | None
Total recording duration in seconds
active : bool | None
Whether the ROI shows calcium activity (has detected peaks)
iei : list[float] | None
Inter-event intervals between calcium peaks (in seconds)
evoked_experiment : bool
Whether this is an optogenetic stimulation experiment
stimulated : bool
Whether this ROI overlaps with the stimulated area
stimulations_frames_and_powers : dict[str, int] | None
Frame numbers and LED powers for stimulation events
led_pulse_duration : str | None
Duration of LED pulse in stimulation experiments
led_power_equation : str | None
Equation to calculate LED power density (mW/cm²)
calcium_sync_jitter_window : int | None
Jitter window (frames) for calcium peak synchrony analysis
spikes_sync_cross_corr_lag : int | None
Maximum lag (frames) for spike cross-correlation synchrony
calcium_network_threshold : float | None
Percentile threshold (0-100) for network connectivity
spikes_burst_threshold : float | None
Threshold (%) for burst detection in spike trains
spikes_burst_min_duration : int | None
Minimum burst duration in seconds
spikes_burst_gaussian_sigma : float | None
Sigma for Gaussian smoothing in burst detection (seconds)
mask_coord_and_shape : tuple[tuple[list[int], list[int]], tuple[int, int]] | None
ROI mask stored as ((y_coords, x_coords), (height, width))
neuropil_mask_coord_and_shape : tuple | None
Neuropil mask: ((y_coords, x_coords), (height, width))
"""
well_fov_position: str = ""
raw_trace: list[float] | None = None
corrected_trace: list[float] | None = None
neuropil_trace: list[float] | None = None
neuropil_correction_factor: float | None = None
dff: list[float] | None = None
den_dff: list[float] | None = None # denoised dff with oasis package
peaks_den_dff: list[float] | None = None
peaks_amplitudes_den_dff: list[float] | None = None
peaks_prominence_den_dff: float | None = None
peaks_height_den_dff: float | None = None
inferred_spikes: list[float] | None = None
inferred_spikes_threshold: float | None = None
den_dff_frequency: float | None = None # Hz
condition_1: str | None = None
condition_2: str | None = None
cell_size: float | None = None
cell_size_units: str | None = None
elapsed_time_list_ms: list[float] | None = None # in ms
total_recording_time_sec: float | None = None # in seconds
active: bool | None = None
iei: list[float] | None = None # interevent interval
evoked_experiment: bool = False
stimulated: bool = False
stimulations_frames_and_powers: dict[str, int] | None = None
led_pulse_duration: str | None = None
led_power_equation: str | None = None # equation for LED power
calcium_sync_jitter_window: int | None = None # in frames
spikes_sync_cross_corr_lag: int | None = None # in frames
calcium_network_threshold: float | None = None # percentile (0-100)
spikes_burst_threshold: float | None = None # in percent
spikes_burst_min_duration: int | None = None # in seconds
spikes_burst_gaussian_sigma: float | None = None # in seconds
# store ROI mask as coordinates (y_coords, x_coords) and shape (height, width)
mask_coord_and_shape: tuple[tuple[list[int], list[int]], tuple[int, int]] | None = None # noqa: E501
# store neuropil mask as coordinates (y_coords, x_coords) and shape (height, width)
neuropil_mask_coord_and_shape: tuple[tuple[list[int], list[int]], tuple[int, int]] | None = None # noqa: E501
# fmt: on

_util.py at commit 336e7b5, under BSD-3-Clause · at the source

Overview

Authors: Wardiya Afshar‐Saber^1,2, Federico M Gasparoli³, Ziqin Yang^1,2, Nicole A Teaney^1,2, Rachel Hobson^1,2, Lahin Lalani^1,2, Gayathri Srinivasan^2,4, Dosh Whye^2,4, Ranit Karmakar³, Elizabeth D Buttermore^2,4, Kellen D Winden^1,2, Cidi Chen^2,4, Mustafa Sahin^1,2,4

ORCID iDs: Wardiya Afshar‐Saber, Federico M Gasparoli, Nicole A Teaney, Lahin Lalani, Gayathri Srinivasan, Dosh Whye, Elizabeth D Buttermore, Kellen D Winden, Mustafa Sahin

Department of Neurology, F.M. Kirby Neurobiology Center, Harvard Medical School, Boston Children's Hospital, Boston, Massachusetts, USA
Rosamund Stone Zander and Hansjoerg Wyss Translational Neuroscience Center, Boston, Massachusetts, USA
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
Human Neuron Core, Boston Children's Hospital, Boston, Massachusetts, USA

Institutions: Boston Children's Hospital (United States); Harvard University (United States)

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), volume 13, issue 26, article e15887

Dates: received 18 August 2025; accepted 24 February 2026; published online 9 March 2026; in print May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/advs.202515887 · PMID 41801223 · PMCID PMC13159138 · OpenAlex W7134279140

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: optical imaging (calcium, voltage, 2-photon) (modality), human (organism), other condition (population)

Methods: Connectivity, Evoked potentials, Statistics, fMRI & imaging, Single-unit activity, calcium imaging, Spectral & time-frequency

Keywords: calcium imaging, machine learning, neurodevelopmental disorders, open‐source, optogenetics, stem cells

MeSH: Calcium*, Induced Pluripotent Stem Cells*, Neurons*, Optogenetics*, Humans (* major topic)

Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: Congressionally Directed Medical Research Programs (W81XWH2110209); Equipment and Core Resources Allocation Committee award at Boston Children's Hospital; Rosamund Stone Zander and Hansjoerg Wyss Translational Neuroscience Center

Citations: cited by 1 paper (Europe PMC); 46 references in the paper

Research resources: RRID:Addgene_120896, the puromycin RRID:Addgene_79049, neomycin RRID:Addgene_99378

Abstract

High‐throughput, single‐cell resolution profiling of neuronal activity is critical for understanding brain function and modeling neurological disorders, yet existing approaches are often limited by scalability and manual workflows. Here, we present an open‐source, scalable imaging and analysis platform that integrates optogenetic stimulation, calcium imaging, automated acquisition, single‐cell and network analyses. The platform enables robust quantification of spontaneous and evoked neuronal activity across hundreds of human stem cell‐derived neurons over multiple timepoints, supporting functional phenotyping at both cellular and network levels. We demonstrate the versatility of the platform across multiple disease‐relevant contexts, including models of CDKL5 Deficiency, SSADH Deficiency, and tuberous sclerosis complex (TSC). Additionally, we generate CRISPR‐Cas9 knock‐in hiPSC lines expressing GCaMP6s and demonstrate partial reversal through pharmacological intervention in TSC. By linking single‐cell dynamics to network‐level measures, this platform provides a generalizable framework for scalable functional phenotyping and high‐throughput screening in human neuronal models.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

fdrgsp/micromanager-gui

License: BSD-3-Clause

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: c6b565b09a3042be0c67c95388c59e2d26ad068d, 8 July 2026

Languages: Python (46)

Size: 61 files, 46 scripts

Software Heritage: not archived

Found in: “Data Availability Statement”

Holds: README, license file, environment (pyproject.toml), tests, continuous integration

Not found: CITATION.cff, documentation

Tools: NumPy (9 files), tifffile (6 files), Cellpose (1 file), pandas (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

48 files

src/micromanager_gui/__init__.py, Python, 15 lines
src/micromanager_gui/__main__.py, Python, 66 lines
src/micromanager_gui/_core_link.py, Python, 332 lines
src/micromanager_gui/_engine.py, Python, 232 lines, 1 match
src/micromanager_gui/_main_window.py, Python, 145 lines
src/micromanager_gui/_menubar/_menubar.py, Python, 354 lines
src/micromanager_gui/_realtime_cellpose_segmentation.py, Python, 197 lines, 1 match
src/micromanager_gui/_slackbot/_mm_slackbot.py, Python, 51 lines
src/micromanager_gui/_slackbot/_slackbot.py, Python, 217 lines
src/micromanager_gui/_slackbot/_slackbot_process.py, Python, 107 lines
src/micromanager_gui/_toolbar/_shutters_toolbar.py, Python, 66 lines
src/micromanager_gui/_toolbar/_snap_live.py, Python, 45 lines
src/micromanager_gui/_widgets/_install_widget.py, Python, 14 lines
src/micromanager_gui/_widgets/_mda_widget/__init__.py, Python, 5 lines
src/micromanager_gui/_widgets/_mda_widget/_arduino/__init__.py, Python, 3 lines
src/micromanager_gui/_widgets/_mda_widget/_arduino/_arduino_led_dialog.py, Python, 407 lines
src/micromanager_gui/_widgets/_mda_widget/_arduino/_arduino_led_widget.py, Python, 87 lines
src/micromanager_gui/_widgets/_mda_widget/_mda_widget.py, Python, 464 lines
src/micromanager_gui/_widgets/_mda_widget/_realtime_cellpose_segmentation_wdg.py, Python, 229 lines
src/micromanager_gui/_widgets/_mda_widget/_save_widget.py, Python, 229 lines
src/micromanager_gui/_widgets/_mm_console.py, Python, 144 lines, 2 matches
src/micromanager_gui/_widgets/_snap_live_buttons.py, Python, 47 lines
src/micromanager_gui/_widgets/_stage_control.py, Python, 98 lines
src/micromanager_gui/_widgets/_viewers/__init__.py, Python, 4 lines
src/micromanager_gui/_widgets/_viewers/_mda_viewer/__init__.py, Python, 3 lines
src/micromanager_gui/_widgets/_viewers/_mda_viewer/_data_wrappers.py, Python, 121 lines, 2 matches
src/micromanager_gui/_widgets/_viewers/_mda_viewer/_mda_save_button.py, Python, 50 lines
src/micromanager_gui/_widgets/_viewers/_mda_viewer/_mda_viewer.py, Python, 81 lines
src/micromanager_gui/_widgets/_viewers/_preview_viewer/__init__.py, Python, 3 lines
src/micromanager_gui/_widgets/_viewers/_preview_viewer/_preview_save_button.py, Python, 70 lines
src/micromanager_gui/_widgets/_viewers/_preview_viewer/_preview_viewer.py, Python, 183 lines
src/micromanager_gui/_writers/__init__.py, Python, 6 lines
src/micromanager_gui/_writers/_ome_tiff.py, Python, 129 lines
src/micromanager_gui/_writers/_tiff_sequence.py, Python, 119 lines
src/micromanager_gui/readers/__init__.py, Python, 6 lines
src/micromanager_gui/readers/_ome_zarr_reader.py, Python, 247 lines
src/micromanager_gui/readers/_tensorstore_zarr_reader.py, Python, 267 lines
test_burst_plots.py, Python, 112 lines
tests/conftest.py, Python, 56 lines
tests/test_custom_sequence.py, Python, 121 lines
tests/test_gui.py, Python, 63 lines
tests/test_mda_viewer.py, Python, 122 lines
tests/test_readers_writers.py, Python, 131 lines
tests/test_save_widget.py, Python, 103 lines
tests/test_stage_widget.py, Python, 20 lines
wip_batch_analysis.py, Python, 110 lines
LICENSE, License, 28 lines
README.md, Text, 185 lines

At the source: github.com/fdrgsp/micromanager-gui

fdrgsp/cali

License: BSD-3-Clause

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 336e7b59d0df7940ddc93d04b4f691b1303cfc3f, 11 June 2026

Languages: Python (175), Jupyter (4)

Size: 1,629 files, 179 scripts

Software Heritage: not archived

Found in: the text, “Introduction”

Holds: README, license file, environment (pyproject.toml), tests, continuous integration, 4 notebooks

Not found: CITATION.cff, documentation

Tools: NumPy (82 files), tifffile (20 files), pandas (10 files), SciPy (8 files), Matplotlib (5 files), Numba (3 files), scikit-image (3 files), Cellpose (2 files), PyTorch (2 files), scikit-learn (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

181 files

_dev/_csv_bar_plot.py, Python, 559 lines
_dev/_json_to_db.py, Python, 729 lines
_dev/_to_csv.py, Python, 983 lines
_dev/correlations.py, Python, 168 lines
_dev/demo.ipynb, Jupyter, 77 lines
_dev/example_load_from_json.py, Python, 21 lines
_dev/examples_run_analysis_from_database.ipynb, Jupyter, 47 lines
_dev/examples_run_analysis_from_directories.ipynb, Jupyter, 96 lines
examples/example_cali_run.py, Python, 74 lines
examples/example_cali_run_imported_labels.py, Python, 90 lines
examples/example_cali_run_tiff_collection_imported_labels.py, Python, 115 lines
examples/example_create_experiment.py, Python, 74 lines
examples/example_database_to_csv.py, Python, 157 lines
examples/example_manual_run.py, Python, 115 lines
examples/example_save_labels.py, Python, 7 lines
examples/examples_plot_from_database.ipynb, Jupyter, 55 lines
examples/tiff_collection_database_example.py, Python, 107 lines
examples/tiff_collection_example.py, Python, 73 lines
src/cali/__init__.py, Python, 17 lines
src/cali/__main__.py, Python, 202 lines
src/cali/_constants.py, Python, 179 lines
src/cali/analysis/__init__.py, Python, 14 lines
src/cali/analysis/_analysis_runner.py, Python, 407 lines, 1 match
src/cali/analysis/_cluster_analysis.py, Python, 164 lines
src/cali/analysis/_fov_analysis.py, Python, 502 lines
src/cali/analysis/_fov_analysis_parallel.py, Python, 626 lines
src/cali/analysis/_fov_metrics.py, Python, 1,562 lines, 1 match
src/cali/analysis/_trace_analysis.py, Python, 290 lines
src/cali/detection/__init__.py, Python, 5 lines
src/cali/detection/_batch_cellpose/__init__.py, Python, 3 lines
src/cali/detection/_batch_cellpose/_batch_segmentation_multiprocessing.py, Python, 269 lines
src/cali/detection/_detection_runner.py, Python, 400 lines
src/cali/extraction/__init__.py, Python, 5 lines
src/cali/extraction/_caiman.py, Python, 1 line
src/cali/extraction/_extraction_runner.py, Python, 875 lines
src/cali/extraction/_neuropil.py, Python, 225 lines
src/cali/extraction/_oasis_suite2p.py, Python, 170 lines
src/cali/extraction/_util.py, Python, 128 lines
src/cali/gui/__init__.py, Python, 5 lines
src/cali/gui/_analysis_gui.py, Python, 1,463 lines, 1 match
src/cali/gui/_cali_gui.py, Python, 3,194 lines, 1 match
src/cali/gui/_detection_gui.py, Python, 610 lines
src/cali/gui/_extraction_gui.py, Python, 646 lines, 1 match
src/cali/gui/_fov_table.py, Python, 60 lines
src/cali/gui/_image_viewer.py, Python, 702 lines
src/cali/gui/_import_labels_dialog.py, Python, 448 lines
src/cali/gui/_init_dialog.py, Python, 192 lines
src/cali/gui/_plate_map.py, Python, 627 lines
src/cali/gui/_plate_plan_wizard.py, Python, 154 lines
src/cali/gui/_pygraph_plot_widgets.py, Python, 1,333 lines
src/cali/gui/_run_selection_dialog.py, Python, 178 lines
src/cali/gui/_run_widget.py, Python, 568 lines
src/cali/gui/_runs_panel.py, Python, 1,059 lines
src/cali/gui/_save_as_widgets.py, Python, 200 lines
src/cali/gui/_tiff_collection_widget.py, Python, 535 lines
src/cali/gui/_util.py, Python, 560 lines
src/cali/logger/__init__.py, Python, 6 lines
src/cali/logger/_logger.py, Python, 38 lines
src/cali/plot/__init__.py, Python, 27 lines
src/cali/plot/_main_plot.py, Python, 1,325 lines
src/cali/plot/_multi_wells_plots/__init__.py, Python, 116 lines
src/cali/plot/_multi_wells_plots/_calcium_peaks.py, Python, 528 lines
src/cali/plot/_multi_wells_plots/_cell_properties.py, Python, 258 lines
src/cali/plot/_multi_wells_plots/_dimensionality_reduction.py, Python, 770 lines
src/cali/plot/_multi_wells_plots/_evoked_activity.py, Python, 199 lines
src/cali/plot/_multi_wells_plots/_inferred_spikes.py, Python, 747 lines
src/cali/plot/_multi_wells_plots/_util.py, Python, 907 lines, 1 match
src/cali/plot/_single_wells_plots/burst/__init__.py, Python, 19 lines
src/cali/plot/_single_wells_plots/burst/_plot_burst_activity.py, Python, 1,395 lines
src/cali/plot/_single_wells_plots/calcium_traces/_plot_calcium_traces_data.py, Python, 479 lines
src/cali/plot/_single_wells_plots/calcium_traces/_plot_neuropil_traces.py, Python, 461 lines
src/cali/plot/_single_wells_plots/cluster/_plot_cluster_analysis.py, Python, 793 lines
src/cali/plot/_single_wells_plots/correlation/_plot_calcium_traces_correlation.py, Python, 446 lines
src/cali/plot/_single_wells_plots/correlation/_plot_connectivity.py, Python, 650 lines
src/cali/plot/_single_wells_plots/correlation/_plot_evoked_correlation_synchrony.py, Python, 1,642 lines, 2 matches
src/cali/plot/_single_wells_plots/correlation/_plot_inferred_spike_synchrony.py, Python, 326 lines
src/cali/plot/_single_wells_plots/correlation/_plot_spike_max_lag_correlation.py, Python, 607 lines
src/cali/plot/_single_wells_plots/correlation/_plot_spike_max_lag_values.py, Python, 358 lines
src/cali/plot/_single_wells_plots/evoked/_plot_evoked_experiment_data_plots.py, Python, 1,462 lines
src/cali/plot/_single_wells_plots/evoked/_stimulation_area.py, Python, 277 lines
src/cali/plot/_single_wells_plots/metrics/_plot_calcium_amplitudes_and_frequencies_data.py, Python, 374 lines
src/cali/plot/_single_wells_plots/metrics/_plot_calcium_peaks_iei_data.py, Python, 247 lines
src/cali/plot/_single_wells_plots/metrics/_plot_cell_size.py, Python, 213 lines
src/cali/plot/_single_wells_plots/metrics/_plot_inferred_spikes_frequency_data.py, Python, 182 lines
src/cali/plot/_single_wells_plots/raster/_plot_calcium_peaks_raster_plots.py, Python, 497 lines
src/cali/plot/_single_wells_plots/raster/_plot_inferred_spike_raster_plots.py, Python, 639 lines
src/cali/plot/_single_wells_plots/spikes/_plot_inferred_spikes.py, Python, 653 lines
src/cali/plot/_util.py, Python, 343 lines
src/cali/readers/__init__.py, Python, 12 lines
src/cali/readers/_ome_zarr_reader.py, Python, 268 lines
src/cali/readers/_tensorstore_zarr_reader.py, Python, 292 lines
src/cali/readers/_tiff_collection_reader.py, Python, 527 lines
src/cali/runner/__init__.py, Python, 5 lines
src/cali/runner/_cali_runner.py, Python, 2,100 lines
src/cali/sqlmodel/__init__.py, Python, 67 lines
src/cali/sqlmodel/_data_to_plate.py, Python, 110 lines
src/cali/sqlmodel/_db_to_plate_map.py, Python, 105 lines
src/cali/sqlmodel/_db_to_useq_plate.py, Python, 152 lines
src/cali/sqlmodel/_model.py, Python, 2,055 lines, 2 matches
src/cali/sqlmodel/_model_graph.py, Python, 35 lines
src/cali/sqlmodel/_plate_map_util.py, Python, 59 lines
src/cali/sqlmodel/_useq_plate_to_db.py, Python, 312 lines
src/cali/sqlmodel/_util.py, Python, 666 lines, 2 matches
src/cali/sqlmodel/_visualize_experiment.py, Python, 465 lines, 1 match
src/cali/util/__init__.py, Python, 69 lines
src/cali/util/_database_to_csv.py, Python, 1,559 lines
src/cali/util/_util.py, Python, 852 lines
tests/__init__.py, Python, 1 line
tests/conftest.py, Python, 367 lines
tests/test_analysis_threading.py, Python, 356 lines
tests/test_analysis_util.py, Python, 912 lines
tests/test_auto_match.py, Python, 145 lines
tests/test_bar_plot_condition_ordering.py, Python, 121 lines
tests/test_basic.py, Python, 27 lines
tests/test_calcium_burst_bar_plots.py, Python, 426 lines
tests/test_calcium_burst_plot.py, Python, 145 lines
tests/test_cali_runner.py, Python, 4,810 lines
tests/test_ccg_improvements.py, Python, 259 lines
tests/test_cell_properties_plots.py, Python, 102 lines
tests/test_cli.py, Python, 92 lines
tests/test_cluster_analysis.py, Python, 184 lines
tests/test_cluster_plots.py, Python, 1,052 lines
tests/test_condition_colors.py, Python, 283 lines
tests/test_conditions_dialog.py, Python, 176 lines
tests/test_connectivity_plot.py, Python, 1,005 lines
tests/test_correlation_vs_synchrony.py, Python, 480 lines
tests/test_cross_correlation_matrix.py, Python, 175 lines
tests/test_custom_model_persistence.py, Python, 125 lines
tests/test_data/data_and_db_for_tests/rebuild_test_db.py, Python, 192 lines
tests/test_database_to_csv.py, Python, 876 lines
tests/test_detection_gui.py, Python, 208 lines
tests/test_dimensionality_reduction_plots.py, Python, 928 lines
tests/test_enable_calcium_spikes.py, Python, 629 lines
tests/test_evoked_activity_plots.py, Python, 227 lines
tests/test_evoked_experiment_data_plots.py, Python, 212 lines
tests/test_export_group.py, Python, 393 lines
tests/test_export_only_option.py, Python, 469 lines
tests/test_extraction_util.py, Python, 309 lines
tests/test_fov_analysis.py, Python, 910 lines
tests/test_gui.py, Python, 1,740 lines
tests/test_gui_database_only.py, Python, 574 lines
tests/test_gui_export.py, Python, 413 lines
tests/test_gui_roi_highlight.py, Python, 189 lines
tests/test_image_viewer_highlight.py, Python, 372 lines
tests/test_import_labels.py, Python, 116 lines
tests/test_import_labels_dialog.py, Python, 109 lines
tests/test_inferred_spike_thresholded_plots.py, Python, 350 lines
tests/test_inferred_spikes_burst_bar_plots.py, Python, 716 lines
tests/test_manual_run.py, Python, 139 lines
tests/test_multi_well_bar_plot.py, Python, 240 lines
tests/test_multi_well_bar_plots_integration.py, Python, 551 lines
tests/test_multi_well_plot_util.py, Python, 376 lines
tests/test_natural_sort.py, Python, 237 lines
tests/test_neuropil.py, Python, 291 lines
tests/test_numba_threading.py, Python, 111 lines
tests/test_oasis_suite2p.py, Python, 94 lines
tests/test_plate_map.py, Python, 354 lines
tests/test_plot.py, Python, 239 lines
tests/test_plot_display_integration.py, Python, 158 lines
tests/test_plot_legends.py, Python, 692 lines
tests/test_plot_util.py, Python, 564 lines
tests/test_plot_widgets.py, Python, 688 lines
tests/test_readers.py, Python, 597 lines
tests/test_results.py, Python, 1,378 lines
tests/test_run_widget_comprehensive.py, Python, 443 lines
tests/test_runner_csv_export.py, Python, 1,486 lines
tests/test_runner_edge_cases.py, Python, 1,124 lines
tests/test_runs_panel.py, Python, 2,060 lines
tests/test_save_as_widgets.py, Python, 127 lines
tests/test_saved_segmentation_selection.py, Python, 109 lines
tests/test_settings_save_load.py, Python, 809 lines
tests/test_single_roi_correlation_filter.py, Python, 207 lines
tests/test_single_well_plot_interactions.py, Python, 1,702 lines
tests/test_spike_jitter_parameter.py, Python, 197 lines
tests/test_sqlmodel.py, Python, 2,507 lines
tests/test_thresholded_spike_performance.py, Python, 196 lines
tests/test_tiff_collection_widget.py, Python, 134 lines
tests/test_util.py, Python, 781 lines
tests/test_with_cellpose.py, Python, 79 lines
LICENSE, License, 28 lines
README.md, Text, 793 lines

At the source: github.com/fdrgsp/cali

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
225 scripts, each with its path and the digest of its content;
19 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

figshare:29599163, at figshare; found in “Data Availability Statement”

Data Availability Statement

The data that support the findings of this study are openly available in FigShare at (https://doi.org/10.6084/m9.figshare.29599163). All data reported in this paper will be shared by the lead contact by request. All code is available on github (https://github.com/fdrgsp/micromanager‐gui (https://github.com/fdrgsp/micromanager-gui)).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 6 keywords, 5 MeSH terms, 3 funders, 41 references, 3 RRIDs.

Cite

This paper

Afshar‐Saber, W., Gasparoli, F. M., Yang, Z., Teaney, N. A., Hobson, R., Lalani, L., Srinivasan, G., Whye, D., Karmakar, R., Buttermore, E. D., Winden, K. D., Chen, C., & Sahin, M. (2026). An Open-Source Pipeline for Calcium Imaging and All-Optical Physiology in Human Stem Cell-Derived Neurons. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 13(26), e15887. https://doi.org/10.1002/advs.202515887

BibTeX

@article{afsharsaber2026open, author = {Afshar‐Saber, Wardiya and Gasparoli, Federico M and Yang, Ziqin and Teaney, Nicole A and Hobson, Rachel and Lalani, Lahin and Srinivasan, Gayathri and Whye, Dosh and Karmakar, Ranit and Buttermore, Elizabeth D and Winden, Kellen D and Chen, Cidi and Sahin, Mustafa}, title = {{An Open-Source Pipeline for Calcium Imaging and All-Optical Physiology in Human Stem Cell-Derived Neurons}}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, year = {2026}, month = mar, volume = {13}, number = {26}, pages = {e15887}, publisher = {Wiley}, issn = {2198-3844}, doi = {10.1002/advs.202515887}, url = {https://doi.org/10.1002/advs.202515887}, pmid = {41801223}, pmcid = {PMC13159138} }

RIS

TY - JOUR AU - Afshar‐Saber, Wardiya AU - Gasparoli, Federico M AU - Yang, Ziqin AU - Teaney, Nicole A AU - Hobson, Rachel AU - Lalani, Lahin AU - Srinivasan, Gayathri AU - Whye, Dosh AU - Karmakar, Ranit AU - Buttermore, Elizabeth D AU - Winden, Kellen D AU - Chen, Cidi AU - Sahin, Mustafa TI - An Open-Source Pipeline for Calcium Imaging and All-Optical Physiology in Human Stem Cell-Derived Neurons T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany) J2 - Adv Sci (Weinh) PY - 2026 DA - 2026/03/09 VL - 13 IS - 26 SP - e15887 SN - 2198-3844 PB - Wiley DO - 10.1002/advs.202515887 UR - https://doi.org/10.1002/advs.202515887 LA - en ER -

CSL-JSON

{ "id": "10.1002/advs.202515887", "type": "article-journal", "title": "An Open-Source Pipeline for Calcium Imaging and All-Optical Physiology in Human Stem Cell-Derived Neurons", "container-title": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)", "author": [ { "family": "Afshar‐Saber", "given": "Wardiya" }, { "family": "Gasparoli", "given": "Federico M" }, { "family": "Yang", "given": "Ziqin" }, { "family": "Teaney", "given": "Nicole A" }, { "family": "Hobson", "given": "Rachel" }, { "family": "Lalani", "given": "Lahin" }, { "family": "Srinivasan", "given": "Gayathri" }, { "family": "Whye", "given": "Dosh" }, { "family": "Karmakar", "given": "Ranit" }, { "family": "Buttermore", "given": "Elizabeth D" }, { "family": "Winden", "given": "Kellen D" }, { "family": "Chen", "given": "Cidi" }, { "family": "Sahin", "given": "Mustafa" } ], "container-title-short": "Adv Sci (Weinh)", "volume": "13", "issue": "26", "page": "e15887", "DOI": "10.1002/advs.202515887", "PMID": "41801223", "PMCID": "PMC13159138", "ISSN": "2198-3844", "publisher": "Wiley", "URL": "https://doi.org/10.1002/advs.202515887", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 9 ] ] } }

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[6] doi:10.1016/j.isci.2026.116206 [code]: Gut distension evokes rapid neural dynamics in vagal and hindbrain populations of larval zebrafish.
Journal: iScience
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[7] doi:10.1038/s41598-026-57519-w [code]: Automated segmentation of neurons and spinal cord structures in immunofluorescence images using SpineDL.
Journal: Scientific reports
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[8] doi:10.1038/s41467-026-71418-8 [code]: Deep visual proteomics uncovers nociceptor diversity and pain targets.
Journal: Nature communications
In common: tifffile, Numba, scikit-image, 5 other tools, 1 reference
[9] doi:10.1038/s42003-026-10063-9 [code]: Conserved Kir channel mechanisms governing intrinsic excitability in human and rodent parvalbumin neurons.
Journal: Communications biology
In common: Cellpose, tifffile, scikit-image, 5 other tools
[10] doi:10.1038/s41586-026-10323-y [code]: Genetically encoded assembly recorder temporally resolves cellular history.
Journal: Nature
In common: Cellpose, tifffile, scikit-image, 5 other tools

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