Company: A cell therapy process development team
Lab staff at a cell therapy process development team faced significant inefficiencies in handling incoming requests and sample data from external systems like iLab. Ginny, a busy lab technologist, previously downloaded CSVs from iLab for complex requests, such as a PacBio sequencing project with twelve tissue samples. She spent an hour manually cleaning duplicate 'amplicon size' columns, adding project codes by hand, and duplicating rows for each sample, leading to data-entry errors and time-consuming workflows.
Teams using Request Manager and Sample Manager had to manually reconcile complex iLab exports with duplicate columns, lack of explicit form identifiers, and the need for tedious row creation for multiple samples. This caused errors, inefficiency, and delayed visibility into incoming projects. Researchers also spent excessive time manually linking compound data, tracking inventory, running calculations, and generating reports across multiple sheets and protocols.
Bulk updates from collaborators compounded issues: weekly Excel files from hospitals in varying formats required hours of reformatting columns, importing spreadsheets, and linking samples to patients across lab sheets, creating redundant data silos with OneDrive master workbooks. In cell therapy workflows, sample traceability, compliance, and real-time insights were challenging during transition to clinical operations, with manual spreadsheet tracking risking errors and delays.
qPCR operations struggled with slow 3-day reporting, implementation blockers, and inability to model multiplex assays, multiple tests per sample, and automated control handling. Data integrity issues persisted in Sample and Test Trackers, with manual entry, limited audit visibility, and inconsistent reporting.
Scispot introduced an automated iLab-to-Scispot integration that ingests iLab exports or API payloads, applies configurable mapping rules - including duplicate-field consolidation, multi-target routing, and row-multiplication based on sample counts - and populates Request and Sample Managers via secure APIs. Ginny now opens Scispot, selects the 'PacBio Intake v2' mapping template, uploads the CSV, and the system instantly detects duplicate 'amplicon size' columns, keeps the one with data, discards blanks, tags the request, and auto-generates twelve rows in Sample Manager. She reviews a preview and confirms, creating linked entries in seconds.
The enhanced iLab Integration & Mapping module resolves duplicate columns via question IDs or smart blank-field handling, routes fields to both managers, auto-generates sample rows, and captures form metadata like request type. Users drag CSVs onto the page; Scispot flags duplicates, suggests merging, expands rows based on sample counts (e.g., twelve for 'Number of Slides'), and tags entries with form names like 'PacBio' or 'GeoMx' for instant filtering in Request Manager.
For end-to-end experiments, Scispot automates CSV imports, ID look-ups, inventory consumption, Jupyter script execution, graph generation, and sign-off. Madeleine imports compound prep and bottle CSVs, matches ChemicalIDs automatically, deducts consumables via workflow, uploads GC results, runs 'Sample Averaging' scripts for processed data, generates charts with prompts, and assigns for review with locking.
Bulk Import & Mapping handles collaborator spreadsheets: drag files into the wizard, apply per-study templates for column renaming, transformation, de-duplication; import new/updated rows to Specimen Manager with auto-links to Patient Manager, store raw files for audit, and sync to OneDrive. In qPCR, multi-test sample records support replicate plate-mapping, marker registries for multiplex dyes, auto-controls, and blocker trackers. Sample Manager logs specimens once for multiple assays, auto-assigns codes, generates replicates inheriting accession numbers.
Compliance workflows include status tracking, QC alerts, Part-11 e-signatures via SSO, auto-exports to EQMS, and KPI reports. Scans update statuses in real-time (e.g., 'Received' to 'Sectioned'), flag expired reagents, and generate reports showing turnaround improvements. Enhanced audit trails, recycle bins, schema locking, automated forms populate trackers, and AI-driven COA generation centralize data with historical migrations.
The integration eliminates repetitive manual work, ensures data accuracy, accelerates request intake, and provides immediate visibility. Success metrics target percentage of iLab requests imported via integration vs. manual, monthly active users of the feature, and mapping templates created. Impact includes average time per request intake under 10 minutes, reduction in data-entry errors, and turnaround from iLab submission to visibility. Technical targets: API success ≥99%, processing <2 minutes for 5MB files, uptime ≥99.5%.
Business goals call for 60% reduction in manual data-entry time within six months, 90%+ accuracy in imports within three months, onboarding five workflows (PacBio, Cosmic, Geomics, RNA-scope, Histology) in one quarter, sample-intake <24 hours, and 50% fewer support tickets. Adoption metrics target 70% of requests via module in one month, >10 weekly imports per lab in three months. Impact: 60% reduction in time per request, <1% overwrite incidents, 95% satisfaction. Processing <30 seconds for 10,000 rows.
Experiment setup targets 50% time reduction via CSV imports, 90% inventory accuracy with auto-deductions, 80% script adoption, 60% cut in reporting time, <2% ID mismatch errors. For bulk imports, goals include 80% time reduction per batch, ≥95% accuracy, capacity from 600 to 3,000 weekly samples, ≥90% adoption. qPCR targets report turnaround from 72h to <24h, 60% entry reduction, >95% dye mapping accuracy, 90% unblock rate in 10 minutes, 100% LIMS migration in 12 weeks, >80% WAU.
Compliance results target 80% trace time cut, Part-11 compliance, weekly reports <5 min effort, <1% reagent failures. Sample trace from >30 min to <5 min, ≥75% failure reduction, ≥20% faster turnaround. Sentera goals: 50% entry time cut, 80% data-loss reduction, 90% COA automation, 30% user increase, 100% historical migration. Overall, teams processed double volume error-free, with processes from hours to moments, freeing focus on science.
Success metrics target percentage of iLab requests imported via integration vs. manual, monthly active users of the feature, and mapping templates created. Impact includes average time per request intake under 10 minutes, reduction in data-entry errors, and turnaround from iLab submission to visibility. Technical targets: API success ≥99%, processing <2 minutes for 5MB files, uptime ≥99.5%.
Impact: 60% reduction in time per request, <1% overwrite incidents, 95% satisfaction. Processing <30 seconds for 10,000 rows.
