From Hemocytometer to Lens-Free Chips: What Has Evolved in 150 Years of Cell Counting?
In life science laboratories, a familiar scene often unfolds: in front of a microscope, a researcher places a drop of cell suspension onto a hemocytometer, carefully observing grid by grid, recording numbers, and calculating cell concentration. This was once a standard, daily routine for countless lab personnel.
While it seems like merely "counting cells," it dictates critical decisions in cell culture, drug discovery, and cell therapy.
Today, let us trace the historical timeline to explore how cell counting has evolved step-by-step from "counting grid lines" to "lens-free chip technology."
The development of modern cell counting technology dates back to the 19th century.
French scientist Louis-Charles Malassez invented the hemocytometer, combining microscopic observation with a standardized grid scale. This innovation enabled researchers to count cells within a fixed volume and calculate sample concentration through proportional scaling.
Because it was straightforward, intuitive, and low-cost, this method remained in widespread use for nearly a century.
(Hemocytometer)
As life science research deepened, the inherent limitations of manual counting became increasingly apparent:
High Dependence on Operator Experience: Judgments regarding cell boundaries, cell clumps, debris, and abnormal cells vary significantly among different personnel.
Limited Field of View: When dealing with unevenly distributed, aggregated, or heterogeneous samples, relying on a few microscopic fields often fails to accurately represent the overall sample status.
In the mid-20th century, the emergence of the Coulter Principle fundamentally changed how cells were counted.
This technology utilizes the change in electrical resistance generated as cells pass through a micro-aperture:
(Coulter Counter Working Principle)
Compared to manual counting, electrical impedance offers significantly faster detection speeds and higher automation, enabling continuous analysis of large sample volumes.
Consequently, it gained widespread application in blood analysis, cell culture, and industrial bioprocess monitoring.
Nevertheless, impedance-based detection carries certain limitations: because it relies primarily on cell volume and electrical properties, its capacity to capture detailed information on cell morphology, structural alterations, and complex phenotypic traits remains restricted.
In recent years, the rapid advancement of artificial intelligence—particularly deep learning—has unlocked new possibilities for cellular image analysis.
By training on vast datasets, AI models can learn complex cellular features and enhance recognition capabilities in challenging samples, transitioning cell analysis from simple "counting" toward deeper "understanding."
However, even with continuously optimized algorithms, optical imaging itself remains a primary factor constraining analytical performance.
In high-throughput cell analysis scenarios, conventional optical systems continue to face fundamental challenges:
How to maintain sufficient resolution while observing larger sample areas?
How to minimize the physical constraints imposed by complex optical paths during rapid analysis of large cell populations?
Lens-Free Imaging Technology emerged to address these exact requirements—dispensing with traditional lenses and allowing cells to project images directly.
(Lens-Free Imaging Principle)
Key advantages of this technological approach include:
Achieving a significantly larger effective imaging field;
Eliminating complex optical components;
Enhancing overall system stability;
Providing an ideal fit for automated, high-throughput analysis applications.
Driven by the growing demand for higher cell analysis efficiency and superior data quality, JLM-Lifetech has integrated lens-free imaging technology with artificial intelligence to launch the BC1600 Series Cell Counter and Morphological Analyzer.
Utilizing Vertical Charge Transfer (VPS) technology, the BC1600 Series performs direct sample imaging via a specialized semiconductor chip, pairing it with intelligent algorithms for comprehensive cell identification and analysis.
Beyond merely "counting cells," the BC1600 Series delivers deep insights into overall cellular health and characteristics.
In practical applications, the system can be used for:
Cell counting
Confluency calculation for adherent cell cultures
Cell morphology analysis
Multi-parametric statistical charting and reporting based on cell morphology
Cell growth dynamics and tracking studies
...
The BC1600 Series offers multi-parametric cell filtering, allowing users to selectively analyze specific cell populations of interest for deeper research.
For biopharmaceuticals, cell culture, scientific research, and quality control, the BC1600 Series provides more stable, standardized data acquisition—reducing human variability and boosting experimental reproducibility.
At the same time, integrated AI algorithms automate workflows to minimize operation complexity, allowing researchers to focus on experimental design and scientific discovery.
For example, if a user is interested in analyzing tumor cell area, they can utilize the custom user-trained AI feature to train an algorithm specifically designed to calculate cell surface area.
(User self-trained cell area calculation)
The BC1600 Series comes in two models: the standard high-throughput version (BC1600 Auto) and the portable version (BC1600 Lite), catering to diverse environments from high-throughput research labs to point-of-care/field testing.
Resolution: 600 Million Pixels (600 MP)
Detection Channels: 2 – 4 Channels
Particle Size Range: 1 μm – 65 μm
Concentration Range: 1 ×10^4 -1×10^8 cells/mL
Features: Automated sampling, automated imaging, automated staining, automated washing
Resolution: 100 Million Pixels (100 MP)
Detection Channels: 1 Channel
Particle Size Range: 1 μm – 65 μm
Concentration Range: 1 ×10^4 -1×10^8 cells/mL
Features: Automated sampling, automated imaging, automated washing
