Automated microscopes
for accelerating progress
in AI × Biology

open, accessible and scalable
from deploying the latest technologies
to self-driving labs

Microscopes
Squid + Microscope

The core Squid+

Fully motorized
Large field of view (FN of 25/26.5)
Laser autofocus
5 channel LED or laser epi-illumination
Motorized filter wheels (8 positions)
Stage top incubator options
Auto immersion media dispensing
Open-source software

The Squid +

Integrated Systems & Solutions

Spinning disk confocal
Line ReScan confocal
Patterned illumination
(coming soon)
Fluidics system for spatial omics
Robotic plate loading for high content screening
Automation ready with SiLA2 drivers

Open Software

"I have used quite a lot of different imaging softwares in the past year. Yours was really excellent and easy to use. Just enough detail and visually easy to navigate."
- Andrew Lu from Elowitz Lab, Caltech

Towards self-driving labs

At Cephla, we're actively building towards the vision of self-driving labs with our bespoke platforms - enabling labs to multiply throughput and establish "lab-in-a-loop" workflows. Our fit for purpose hardware and open-source software provide researchers the agility needed to rapidly innovate and scale. Working closely with our collaborators and partners, we're excited about the new era of scientific research and drug discovery.

Example Applications

High Content Screening
3D cell culture
Expansion Microscopy
Spatial Biology
OPS
Live Cell Imaging
Microarrays
Microarrays
High Content
Live Cell
Spatial Biology
Microarrays
Bioluminesence
Organoids
Cell Paint
High Content

Recent Publications from Cephla microscope users

  • Frey, Benjamin, et al. "Single-cell morphological profiling reveals insights into cell death." bioRxiv (2025): 2025-01.
  • Li, H., et al. (2025). Octopi 2.0: Open and Scalable Microscopy Platform for AI-powered Diagnostic Applications. medRxiv. https://doi.org/10.1101/2025.03.21.25324364
  • Zhang, Nicholas, et al. "Graph-Based Spatial Proximity of Super-Resolved Protein–Protein Interactions Predicts Cancer Drug Responses in Single Cells" Cellular and Molecular Bioengineering, 17, 467–490, (2024).
  • Hall, R. Nelson, et al. "A genetic and microscopy toolkit for manipulating and monitoring regeneration in Macrostomum lignano." Cell Reports 43.11 (2024).
  • Zhang, Qing, et al. "Ice gliding diatoms establish record-low temperature limit for motility in a eukaryotic cell." bioRxiv (2024): 2024-11.
  • Larson, Adam G., et al. "Inflation-induced motility for long-distance vertical migration." Current Biology 34.22 (2024): 5149–5163.
  • Ogunlade, B., et al. (2024). Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy. PNAS. DOI:10.1073/pnas.2315670121
  • Dai, T.,  et al. (2023). Culture-Independent Multiplexed Detection of Drug-Resistant Bacteria Using Surface-Enhanced Raman Scattering. ACS sensors, 8(8), 3264-3271. DOI: 10.1021/acssensors.3c01345