Scispot connected intake, library preparation, pooling, and sequencing in one traceable workflow, giving the team a clear view of each sample’s status, history, and downstream outputs.
Scispot brought historical records, active sample data, inventory, and workflows into one system, cutting down cross-checking across spreadsheets, dashboards, and legacy tools.
Scispot gave the computational team API access and audit logging, so lab data could move into cloud and bioinformatics pipelines with a clear change history.
A scientific R&D services organization in translational research runs a full-stack multi-omics lab. The team supports genomics, flow cytometry, sequencing, and computational biology workflows.
Their work includes single-cell genomics, library preparation, sample pooling, and downstream analysis. Wet lab, bioinformatics, and technical infrastructure teams all need to work from the same data.
In this kind of lab, every sample has downstream impact. The team needs clear sample history, reliable data, and tight coordination across functions.
As the lab scaled, its workflows became harder to manage across disconnected tools.
Sample data lived in Excel and shared drives. Operational tracking and dashboards lived in Smartsheet. Inventory was managed in a legacy LIMS and manual trackers. Each tool had a role, but the tools did not connect.
That created a gap between how the lab worked and how its data was managed.
To trace a sample, the team had to piece together records across multiple systems. Intake, library preparation, pooling, and sequencing data were not tied together in one place. Simple questions about sample status, history, or downstream outputs took extra work to answer.
Sequencing workflows added more risk. Index assignment involved manual steps across systems, which increased the chance of errors at a sensitive point in the workflow.
Inventory was also hard to keep clean. Consumables and reagents were split across systems, so teams often had to cross-check records to confirm what was available, what was used, and where materials fit into protocols.
The computational team also needed API access to lab data with clear audit trails. As the work moved closer to clinical and translational use cases, the team needed stronger traceability and data control.
Scispot gave the lab one operating layer for sample tracking, inventory, sequencing workflows, and computational access.
Within a few weeks, the team had a working Scispot environment that matched how the lab runs.
Sample data now lives in one system of record. Instead of hunting through spreadsheets and separate tools, the team can track samples in one place with their full context attached. Historical data and current work sit together, which makes past experiments easier to reference during active work.
The biggest change was sample lineage. Scispot connects each workflow step across intake, capture, library preparation, pooling, and sequencing. Scientists no longer have to rebuild sample history by hand. They can follow a sample’s lifecycle directly in the system, with relationships between steps clearly defined.
Inventory is now managed in one place. Reagents, kits, and other materials that were previously split across systems are consolidated, with clearer visibility into protocol usage. This helps daily lab work and creates a stronger base for future inventory automation.
Sequencing workflows are now more controlled. Index assignment no longer depends on manual copy-paste between systems. The process is structured inside Scispot, which reduces error risk and improves confidence in the data used for downstream analysis.
For the computational team, Scispot provides API access and audit logging. Bioinformatics workflows can connect to lab data in cloud pipelines while keeping a clear record of how data was created and changed.
With Scispot in place, the lab now runs on a more connected data system.
Sample tracking is consistent across the workflow. The team can trace a sample across intake, library preparation, pooling, and sequencing without leaving the platform. This makes it easier to answer questions about sample status, history, and downstream outputs.
Data is no longer scattered across as many systems. Historical Excel records and current operational data now live together, which reduces the need to cross-check external files.
Inventory management is cleaner. The team has a clearer view of materials, protocol usage, and availability. That makes planning easier and removes friction from daily lab work.
Sequencing workflows are more reliable. Removing manual index assignment steps lowers the risk of errors and helps protect sequencing data quality as it moves into computational analysis.
Wet lab and computational teams also work from a better shared data layer. With API access and audit logging, lab data can move into downstream pipelines while preserving a clear change history.
The lab has moved away from disconnected tools and toward one system for scientific operations. The team now spends less time managing data across systems and more time using that data to move projects forward.
The same setup also gives the team room to grow. They can add instrument integrations, advanced reporting, and deeper computational workflows without adding more manual coordination.
