Spiral microfluidic devices for cell separation and sorting in bioprocesses [original]

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Herrmann, N., Neubauer, P., & Birkholz, M. (2019). Spiral microfluidic devices for cell separation and
sorting in bioprocesses. Biomicrofluidics, 13(6), 61501. https://doi.org/10.1063/1.5125264
Niklas Herrmann, Peter Neubauer, Mario Birkholz
Spiral microfluidic devices for cell
separation and sortin
g

in bioprocesses
Accepted manuscript (Postprint) Journal article |

1
Spiral microfluidic devi ces for cell separa tion and sorting in
bioprocesses
N. Herrmann 1,a) , P. Neubauer 1 and M. Birkholz 2

1 Institute of Biotechnology, TU Berlin, Berlin, Ackerstr. 76 , 13355 Berlin, Germany
2 IHP – Leibniz-Institut für innovative Mikroelektronik, Im Technologiepark 25, 15236 Frankfurt ( Oder),
Germany
Inertial microfluidic systems have been arousing interest for medical applications
due to their simple and cost- efficient use. However, comparably small sample
volumes in the µl and ml ran ge have so far prevented efficient applications in
continuous bioprocesses. Nevertheless, recent studies suggest that these systems
are well suited for ce ll separation in bioproc esses because of their facile
adaptability to various reactor sizes and cell types. This review will discu ss
potential applications of inertial microfluidic cell separation systems in
downstream bioprocesses and depic t rec ent advances on inertial microfluidics for
bioprocess in tensification. The review thereby focusses on spiral microchannels
that separate particles at a moderate Reynolds number in a laminar flow
(Re<2300) according to their size by applyi ng lateral hydrodynamic forces. Spiral
microchannels have already been shown to be capable of replacing microf ilters,
extracting dead cel ls and debris in perfusion processes and removing contaminant
microalgae species . Recent advances in parallelization made it possible to process
media on a liter-scale which might pave the way towards industrial applications.

__________________ _______
a) Author to whom correspondence should be addressed: n.herrmann@campus . tu -berlin.de

This is the author’s peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.
PLEASE CITE THIS ARTICLE AS DOI: 10.1063/1.5125264

I. INTRODUCTION
With more continuous bioprocesses being applied industrially, efficient cell separ ation
methods are needed to retain productive cells in the system and thereby increasing process yield.
However, currently used tec hniques like microfiltration and centrifugation show various
drawbacks like membrane-clogging, low scalability and challenges in automatization. Since the
introduction of first commercial cell-sorting FACS (fluorescen ce -activated cell sorting) systems ,
several chip-based microfluidic devices have been developed that offer cost -efficient solutions
for separation and sorting of cells. They can be clas sified into active systems li ke
acoustophoresis, magnetophoresis, dielectrophoresis and deterministic lateral displacement
(DLD) that depend on external force fields , and pass ive systems that include gravitation- and
inertia-based techniques. Passive systems are usually favo red becaus e of the ir lower complexity.
Microfluidic separation techniques have been reviewed recently 1 – 6 with some papers focusing on
inertial devices in particular 7 – 11 . Inertial separation is solely based on channel geometry and
hydrodynamic forces 12 without requiring cell manipulation by external forces which makes it a
robust and easy- to -use method. The most common architectures for inerti al separation are
straight and spiral mic rochannels. Straight microchannels are most commonly u sed for cell
separation for medical purposes. Separation of circulating tumor cells 13 , 14 , red blood cells 15 , 16 and
MCF-7 cells 16 could already be show n. Spiral channels stand out because they all ow processing
at high er flo w rat es of up to 1 l/min 17 due to the ir large channel geometry. The her e presented
channel structures wer e fabricated in polydimet hylsiloxane (PDMS) using soft-lithographic
techniques or in poly(methyl methacrylate) (PMMA) using laser cutters . These materials can
prospectively be combined with semiconductor manuf acturing processes which opens the door
for new lab-on-chip applications wit h elements from both microfluidics and mi croelectronics 18 .
Medical applications of spiral microchannels today are manifold and incl ude isol ation of

circulating tumor cells 19 , 20 , blood cells 21 , 22 and sperm cells 23 , isolating axons from neuronal cell
bodies 24 , cell-cycle synchronization 25 , and blood-plasma separation 26 . As channel dimensions
match typical cell sizes, a variety of different cells lik e mammalian cells, yeast 27 and even
bacteria 28 could be separated. The reproduction of large- scale processes on microfluidic devices,
however, is a cha llenging task. 29 . Currently, a pplying microfluidics in microalgae processes
shows promisi ng results. A siz e dependent separation of microalgae cells with different lipid
content using dielectrophoresis and platforms for growth and oil produ ction analysis 30 , 31 have
already been developed. This paper reviews potential applications of microfluidic cell separ ation
and sorting in bioprocesses, thereby focu sing on spiral channels as they show the highest
potential for implementation in large-scale processes due to their energy eff iciency and facile
scalability.
A. Separation principle in spiral microchannels
In spiral microchannels with a laminar Poiseuille flow , i.e. Reynolds numbers Re < 2300,
three forces may cause a size- dependent separat ion. Shear gradient-induced (i) and wall-induced
lift forces (ii) play important roles for separation in both straight and curved microchannels.
Introducing cur vature to the channel, however, induces a secondary-flow that acceler ates the
arrangement of particles in the equilibrium position. This is caused by a secondary-flow drag (iii)
force, called Dean drag.
The shear gradient-induced lift force is caused by the parabolic velocity profile in the channel
that lea ds to different velocities on either side of the particle. The particle thereby experiences a
force pushing it to are as with lesser relative ve locity differences which can usual ly be found in
the near-wall region. 32 Thus, shear-gradient induced lift forces counteract wall -induced lift forces
and particles in that fluid stream therefore arrange at positions in the channel where these for ces

are in bala nce (Fig. 1). In rectangular channels two of these equilibrium positions can b e
identified. They preferably for m close to the center of the channel’s side walls. The magnitude of
both inertial li ft forces depends on particle size and they become stronger at higher R eynolds
numbers. 8 The shear-gradient lift force F SG can be calculated from

𝐹 𝑆𝐺 = 𝐶 𝑆𝐺 𝜌𝑈 𝑀𝑎𝑥 2 𝑎 3
𝐷 ℎ ,

(1 )

where C SG is the li ft coefficient for the shear gradient li ft force , ρ is the fluid density, U Max is the
fluid’s maximum velocity, a is the particle diameter and D h is the hydraulic diameter that can be
calculated for rectangular channels by 2h×w/(h+w) with h and w being height and width,
respectively. 32 This shows that F SG becomes larger with decreasing channel dimensions which
shows the necessity of microstructures for efficient particle separation.
Wall-induced lift forces result from pressure that is building up in between the particle and
th e wall. Th e particle is slowed down by interactions with the wall an d a force is induced that
directs particles away from the channel wall towards the channel’s center . 32 The wall-induced lift
force F WI can be calculated from

𝐹 𝑊𝐼 = 𝐶 𝑊𝐼 𝜌𝑈 𝑀𝑎𝑥 2 𝑎 6
𝐷 ℎ 4 ,

(2)

where C WI is the lift coefficient for the wall interaction force. 32
Dean flows are also a result of velocity differences within the channel. As fl uid parcels in the
channel center move faster compared to the near-wall region, these parcels are carried towards
the outer wall by the fluid’s inertia , once a curvature is introduced to the channel. This lea ds to a
recirculation of the parcels and thereby a secondary flow is induced in the shape of two counter -

rotating vortices at the top and bottom surface s of the channel (Fig. 1). 32 The Dean fl ow is
characterized by a dimensionless Dean number that is defined as

𝐷𝑒 = 𝑅𝑒 ( 𝐷 ℎ
2𝑅 ) 0.5 ,

(3)

where Re is the Reynolds number and R is the average radius. Thus, smaller radii generate
stronger secondary flows. 8 This secondary flow imparts a drag force on particles that acts
differently on particles with different sizes and thereby improves separation efficiency. 33 The
terminus “separation efficiency” is hereby used in a purely qualitative way for processes with
two or more cel l types that differ in size. It takes into account the purity of each outlet fraction
and the cellular composition of the ori ginal medium as large differences in cellular abundance
should also lead to a higher contamination of the smaller fraction at the outlet. Another important
consideration is the difference in main cell size. As cel l sizes vary a lot, an overlap in cell sizes
be tween two different cel l types will reduce the outlet fraction’s purity. Increasing flow rates
strongly leads to the Dean-flow becoming the dominant force which rather causes dispersion of
the particles than separation. 12 The secondary-flow drag force F D can be calculated by

𝐹 𝐷 = 6 𝜋𝜇𝑎 𝑈 𝑆𝐹 ,

(4)

where µ is the fluid viscosity and U SF =1.8×10 -4 De 1.63 is the velocity of the secondary -flow. 32
Guan et al. 34 examined spiral microchannels with trapezoidal cros s-sections and found that
stronger Dean vortice s were formed on the channel side with bigger depth which lead to an
improved separation.
Additionally to the three mentioned forces, several weak forces also act on the particles.
These forces emerge when par ticles lead, lag or rotate in the fluid stream 8 and are up to several
orders of magnitude weaker than the above mentioned forces, which is why they can usually be

neglected. 32 Rotational lift forces only ge t dominant after an initial equilibrium position is
reached and help particles focusin g near the channel walls’ center . 12 Centrifugal effects play a
minor role as the particles’ and fluid’s densities are too similar. 10

FIG. 1. Major hydrodynamic effects affecting ce ll positioning in curved, rectangular microch annels. Wall -induced lift for ces
( F WI ) push particles towards the channel’s center, whereas shear -induced lift forces ( F SG ) direct particle s towards the side of the
microchannel. By introducing curvature to the channel, a secondary flow is induced which applies a Dean drag ( F D ) on the
particles that supports attaining equili brium position. As all three hydrodynamic forces are size -dependent, differently sized cells
focus at slightly different positions in the channel.

II. APPLICATIONS OF SPIRAL MICROCHANNELS IN BIOPROCESSES
A. Replacement of microfilters
Microfiltration plays an important part in many indus tri al bioprocesses 27 as particles ranging
from 10 nm - 10 µm are difficult to separate fr om a suspension with other common methods like
centrifugation, gravitational settling and adsorption techniques 9 and biotechnologically relevant
organisms typically range in that dimension. However, microfiltration is not flawless as
membrane clogging and fouling occur frequently which drastically reduces efficiency of the
method thr ough e. g. retention of proteolytic enzymes from dead cells. Substitution of membrane

filters involves interru pting the process which increases risks of contamination and accounts for
a major part of operating costs. 27
By incr easing fl ow rate s in their spiral microchannels slightly from 2 ml/min to 6 ml/min,
Warkiani et al. 27 could show that the device switches fr om a cell se paration mode to a ce ll
retention mode as all cells were focused at the inner wall. This all owed them to incor porate
inertial microfluidics into a perfusion bioprocess. Cells where retrieved from the spiral’s inner
outlet and lead back into the bioreactor for further prot ein production, whereas cell -free medium
containing the produced protein and other small particles like cell debris was collected at the
outer outlet and could be used for subsequent protein purification (Fig. 2). Replaci ng microfilters
with spiral microfluidic devices has already been described 35 in 2007 but recent advances show
that inertial microfluidics can reach throughputs at least comparable to mechanical mem brane
filters which can process approximately 10 9 cells/ml. 34 T his is an essential requirement for
industrial applications.
Warkiani et al. 27 could show cell retention for CHO and yeast cells, at the example of
Saccharomyces cerevisiae, in separate approaches (Table I). CHO cel l retention was first
conducted with a single spiral at a flow rate of 6 ml/min and a retention eff iciency of >95%
could be measured for three different cell lines. They then went on to multiplex 8 4 microchips
with four spirals each to retain CHO cells at a flow -rate of 500 ml/min. No change in viability,
morphology and proliferation was observed and by measuring the expression of the shear stress
biomarker c-Fos, it could be shown that no stress response could be detected in the cell which
can probably be expla ined by the short residence time of only <0.1 s on the average withi n the
spiral. Similarly to thi s approach , S. cerevisiae with a concentration of 10 5 cells/ml was retained
at a lower flow rate of 2 ml/mi n based on smaller channel dimensions and cell sizes (3 -5 µm for

yeast compared to 10 – 20 µm for CHO cells). 27 Yeast cells were retained with >90% efficiency
in the single spiral and also in a multiplexed devic e with 180 spiral microchannels that could
process 320 ml/min medium, which represents an improvement compared to commonly used
cellulose acetate and teflon filters. 27
In a different experiment (Table I) , Warkiani and co-workers could show that even cell-cycle
synchronization is possible in spiral microchannels based on size differences in the stages of the
ce ll -cycle. Cells in the G 0 /G 1 phase were separated from G2/M-phase cells, which are generally
larger in size, at a cell concentration of 10 6 cells/ml and a flow rate of 1 ml/min. After the
separation process, smaller cells with diameters <14 µm were enriched more than 2.7 fold at the
outer outlet. It would thereby be possible to retain only highly productive growing cells in the
perfusion process.
In 2017, Kwon et al. 36 act ually in corporated spir al microfluidic devices in pe rfusion
processes for cel l ret ention over a course of 18 – 25 days with peak CHO cel l concentrations of
20 - 30×10 6 cells/ml (Table I) . In their first experiment , the process was run in a 350 ml
bioreactor for 4 days in a batch mode with subsequent perfus ion mode for another 14 days,
applying a perfusion rate of two vessel volumes per day. The goal of the process was IgG 1
production. On day 10 the peak cell concentration of 22.7×10 6 cells/ml was reached with a cell
viability of 99±1% . For cell concentrations <15×10 6 cells/ml, a retention efficiency of 99±2%
could be achieved which dropped to 82±3% for cell concentrations in the ran ge 20 -
23×10 6 cells/ml. Within 18 days, 263 mg IgG 1 were be produced.
Separately from the perfusion processes, Kwon and co-workers investigated cell retention
efficiencies for even higher cell concentrations us ing increased channel dimensions

(1000×260/80 µm). 36 At a flow rate of 4 ml/min, retention efficiencies of >84% could be reached
for a CHO cell concentration of 43.6×10 6 cells/ml (Table I).

FIG. 2. General setup for cell retention with spiral , trapezoidal microchannels in perfusion bioprocesses. Medium from the
bioreactor is pumped through the spiral at a specific flow rate where the dispersed cells (A) get focus ed at the channel’s inner
wall by hydrodynamic effects (B). Cells the n exit the spiral from the inner outlet and ar e lead back into the bioreactor while cell-
free medium is obtained from the spiral’s outer outlet and e. g. used for product recovery (C) .

This shows that inertial microfluidic devices can als o be used for processes with high cell
concentrations although they might probably not be applicable for current high -density processes
with cell concentrations >100×10 6 cells/ml because of the small channel dimensions. By
parallelizing the spir als, high throughputs may be generated which make iner tial microfluidics
more feasible for up-scaling to industrial processes.

B. Separation of live and dead cells
Removing nonviable cells and debris is often a crucial step in biopr ocesses as dead cells can
affect product yield by , for instance, releasing large amounts of proteases into the medium. 37
They can thereby also downgrade the product ’s quality. In CHO bioprocesses , it could be shown
that dead cells make up for up to 30% of the total produced biom ass. 38 Ce ll death in bioreactors
is amongst other s c aused by apoptosis and also by shear stress through stirring and sparging in
the reactor. 37 Current methods for separation of dead cell s include inclined settlers 39 and more
recently also compact settlers. 40
TABLE I. Overview of applied process parameters for presented ap plications of spiral microfluidic devices.

Application

Cell
concentration
[×10 6 cells/ml]

Input flow
rate
[ml/min]

Number
of
spirals

Loops
per
spiral

Particle

Dimensions
[µm]

Separation /
retention
efficiency
[%]

Refer-
ence

Cell retention

1.0

6.0 a

n. a.

CHO

80/130×600 c

>95

Cell retention

10.0

500.0

336 c

n. a.

CHO

80/130×600

n. a.

Cell retention

0.1

2.0 a

n. a.

Yeast

30/70×450

Cell retention

0.1 [g/l]

320.0

180 c

n. a.

Yeast

30/70×450

>90

Cell cycle
synchronization

1.0

n. a.

CHO

80/130×600

n. a.

Cell retention

4.8

1. 0

CHO

80/130×600

Cell retention

43.6

4.0

20 c

CHO

260/80×1000

>84

Live-dead cell
separation

3.5

1.5

CHO

80/130x600

99.7

Live-dead cell
separation

10.0

8.0

2 d

6/8

CHO

80/130×600
200/140×1000

´99.9

Live-dead cell
separation

4.0

6.0

4 c

CHO

80/130×600

99.9

Removal of
contaminants

0.3 – 6.3 e

1.0

Micro-
algae

80/130×600

>90

Parallelization

n. a.

1000.0

20 c

2.5

Micro-
beads

536×3000

Cascading

n. a.

22.0
12.0
7.0

3 d

Micro-
beads

536×3000
336×1800
236×1200

~95

a for a single spiral
b heights of outer walls × wid th of channel’s base ; microchannels with a trapezoidal cross-se ction were used except for the last tw o applications
with microbeads where rectang ular channels were used
c parallelized spirals were us ed.
d cascaded spirals were used.
e P. tricornutum concentrations from 0.3 – 6 × 10 6 cells/ml and a fixed T. suecica concentration of 0.3 × 10 6 cells/ml were used.

Kwon et al. 41 applied inertial microfluidics to separation of nonviable cel ls and cell debris
from viable CHO cells. CHO cell s are the most frequently used hosts for expression of
recombinant proteins, accounting for more than 70% of the total worldwide rec ombinant
proteins. Separation of the generally smaller dead cells in inertial microfluidic systems is
difficult, because viable and nonvia ble cells overlap partly in size which set s a natural limit for
the efficiency of the separation process. 41 The size differ ence is caused by cell- shrinkage in the
early stages of apoptosis which is important for regulating the activity of apo ptotic nucleases and
caspases. 42 In their experiments 41 , Kwon and co -workers focused on maintaining high viable cell
concentrations while concurrently removing as many dead cells as possible.
In a first experiment (Table I), their system was tested for separation of cells <10 µm with
3.5 × 10 6 cells/min, a flow rate of 1.5 ml/min and a split ratio of 0.36 which is defined as outer
outlet flow rate divided by inner outlet flow rate. Dead cel ls and debris were focused at the outer
outlet and a live cell retention effic iency of 99.7% was reached with a dead cell removal
efficiency of 6.1%. They then proceeded to characterize their device by examining the
correlation of cell concentration, flow rate, cell viability and flow split ratio on live cell retention
efficiency, dead cell removal efficiency and dead cell removal purity which they defined as
portion of nonviable cells in the outer outlet (Table II). Especially cell concentration seems to
have a big impact on the separ ation process which can be explained by an increase of cell to cell
interactions in the small microchannel with increasing cell concentration. In their final
experiment, effects of cascading and parallelization on separation efficiency were investi gated.
For cascading, a wide spir al with six coils was connected to the already char acterized narrow
microchannel with 8 coils as a first stage of size -dependent separation (Table I) . Flow rate could
be increased to 8 ml/min and cel l concentration was set to 10 × 10 6 cells/ml. This resulted in a

live cell retention efficiency of 99.9% while the dead cell removal efficiency dropped to 3.4%. In
the para llel approach with four spirals (Table I) and inverted fluid flow at 4 × 10 6 cells/ml cell
concentration and 6 ml/m in flow throug h, the same live cell retention efficiency could be
reached but dead cell removal efficiency dropped even further to 3%. However, even a small
dead cell removal efficiency can have big impacts on bioprocesses a s microfluidic systems can
be run continuously.
TABLE II. Effect of process parameters on separation measures, measured at cell concentrations from 1 - 10x10 6 cells/ml, flow
rates from 0.9 - 1.5 ml/min, viabilities from 30 - 80% and flow split ratios from 0.32 - 0.85 in a spiral microfluidic device with
eight loops, an inner depth of 80 µm, an outer depth of 130 µm and a width of 600 µm. 41

Cell concentrat ion

Input flow rate

Cell viability

Flow split ratio

Live cell retention efficie ncy a

Dead cell removal effici ency b

Dead cell removal pu rity c

- d

a fraction of total viable cells in inner outlet.
b fraction of total dead cells in outer o utlet.
c purity of nonviable cells in outer outl et
d 0 means no correlation was ob served; - means weak n egative correlation; - - means strong negative correlation (separation measur es change
>10 % in measured range); + means weak positive correlation; ++mean s strong positive correlation (separation measures change >10 % in
measured range)

C. Removal of contaminants in microalgae cell cultures
Microalgae cells are becoming more prominent in biotechnological processes, especially as
source of biomass and production hosts for biofuels because of their ability to capture carbon
dioxide. 12 However, processes working with mi croalgae cells are es pecially susceptible to
contaminations, primarily by zooplankton, phytoplankton -lytic bacte ria, virus and other algae
due to di fficult sterilization proce sses. 43 These cross-contaminations with other microalgae
species are often inevitable. Re source competition and secretion of harmful secondary
metabolites thereby leads to drastic decr eases in process yield and product quality. Current
methods to treat these contaminations include microfiltration, addition of chemicals acting
against the pollutant and changes in environmental conditions like harsh changes in pH. 43

In laboratory set -ups, time-consuming and labor -intensive techniques li ke serial dilutions and
selective agar plates are usually applied to reduce contaminations. Syed et al. 12 investigated the
potential of inertia based microfluidic systems on separating the common invading microalgae
Phaeodactylum tricornutum fr om a culture of Tetrasemis suecica cells. After tests with 6 µm and
10 µm microbeads and microalgae cells , they found that the best separation was reached at a
flow rate of 1 ml/min. The fusiform P. tricornutum with approximate diameters of 25.7 ±3.5 µm
was focused at the outer outlet whereas T. suecica wi th a diameter of 10.7 ±0.8 µm exited
through the inner outlet. Both cell types were focused with efficiencies >90%. In a second
experiment (Table I) , cell concentrations of P. tricornutum we re graduall y increased from
0.3 × 10 6 cells/ml to 6 × 10 6 cells/ml while the T. suecica concentration was held at
0.3 × 10 6 cells/ml to investigate the impact of pollutant concentration on the separation process.
At all concentrations, P. tricornutum focusing efficiencies remained over 90% which
demonstrates the inse nsit ivity of the method to the contaminant’s c oncentration. After the
separation, no measurable change in T. suecica vitality could be detected which distinguishes
inertial microfluidic cell separ ation from other common methods for removal of contaminants.
T. suecica was then reinoculated to test the sustainability of the purific ation p rocess and it was
found that P. tricornutum contamination remained suppressed until day 10 and at the end of the
third week, contamination was sti ll fi fty times lower than in unpuri fied samples. By applying the
separation process in a, for instanc e, two-week in terval, P. tricornutum contamination could be
suppressed. However, it was remarked that parallelization of the process is essential for
upscaling to an industrial scale.
Li and co -workers 44 also worked on separating microalgae cells. They used str aight
microchannels to separate cells of the biodiesel producing alga Euglena gracilis based on

different shapes of the same organism to gain a shape -synchronized population. 10 5 cells/min
could thereby be separated using fi ve different o utlets. In a different experiment by Condina et
al. 45 , spiral microchannels were used to separate beer spoilage bacteria from yeast for subsequent
identification using mass spect rometry. Separation efficiencies of >90% were reached at a flow
rate of 1.5 ml/min. They could thereby improve the limit of dete ction for common contaminating
bacteria in the beer industry and reduce the time for detectio n of contaminations.
D. Bead-linked separation of specific cells and proteins
For spiral microchannels, a confine ment ratio of a/D h > 0.0 7 was shown empirically, below
which all particles flow through the outer ou tlet. 46 From this follows that the minimal cell s ize
that can be focused in spiral microfluidic devices is limited by the channel dimensions. Sarkar
et al. 47 developed a method that can evade this problem by linking cells to microbeads of
different size with subsequent mi crofluidic separation. By bindi ng specific cells to antibody -
coated microbeads (10 and 15 µm) with biotin -streptavidin linkages in a si ngle binding step , they
were even able to separate CD4+ from CD8+ T cells and T cells from B cells although these
cells show only mi nimal differences in size (6 – 8 µm). For the latter approach , microbeads were
coated with anti -CD3 and anti-CD19 antibodies, respectively. Their Dean Flow Fractiona tion
Device (channel height was 115 µm) was thereby able to process around 10 5 beads/s. It could be
observed that the focusing position in the chann el was determined by the bigger part of the bead-
cell pair. In case microbead s and cells had the same size, an additive eff ect of the two sizes on
focusing behavior was obser ved. In a different approach, Sarkar and co-workers separated three
specific HIV-antibodies from total IgG-fr actions of serum containing less than 1% antibodies
that they target ed. 47 Smaller microbeads with diameters of 10, 4.5 and 1 µm were used t o
improve surface to mass ratio which in turn improves antibody binding efficiency.

Approximately 95% of all three antigen-specific antibodies were purified with less than 5%
cross-contaminations.
The bead-linked separation approach could be used in the pu rification process of monoclonal
antibodies and for separating small cells like bacteria which has only been done in few cas es. 48 , 28
A cascaded setup could potentially even enable separati on of a large number of different proteins
or similar-sized cells.
III. ADVANCES IN THROUGHPUT
For most industrial processes , cell separation with flow rates in millilit er -scale, as shown by
the previously mentioned applications, are insufficient. A single microchannel, however, can
only process small flow rat es while still maintaining efficient particle separation. Increasing flow
rates in microfluidic devices can therefore only be done effectively by cascading or
parallelization. Efficient upscaling of channel dimensions is restricted by the confinement ratio
and as the biotechnologically relevant group of bacteria is very small (about 1×3 µm for
E. coli 49 ), channel dimensions have to be kept small, too. Cascading has already been applied,
e. g. for separation of cells from blood samples 50 , 51 but the focus rat her lying on rea ching higher
separation efficiency compared to single devices than incr easing throughput. Parallelization is
difficult because in -plane par allelization would occupy too much space and sta cked systems
suffer from differences in inlet pressure depending on the distance of the microchip to the pump.
Since the flow rate is a const itutive factor for reaching want ed equilibrium positions, Miller
et al. 17 developed a modular manifold, enabling equal inl et pressure distribution on a sta ck of 20
spiral microchannels with a tor oidal channel design (Table I) . This allowed them to separate
microbeads with sizes of 45 and 250 µm at a flow rate of 1 l/min which excels p revious

parallelization approaches of inertia -based devices significantly. 27 , 48 They thereby focused ~88%
of the lar ge microbeads in the inner outlet while ~73% of the small particles exited the spiral
through the outer outlet.
In a cascaded approach (Table I) , described in the same paper, Miller an d co -workers used
spirals of three different widths (500, 300, 200 µm) to separate microbeads reaching from 1 –
300 µm. Cascading allowed them to successively remove larger particles down to the smallest
microbeads. To increase recovery rates, two recirculations were performed per separat ion step.
By using a spiral with a comparably large cross- sectional area first, higher flow rates can be
applied. It was also shown empirically in the ir experiments that for large spirals (>300 µm), the
minimum focusing size rather correlates with 0.021356 ×H 1.33623 where H is the channel height. In
this case, an initial flow rate of 22 ml/min was set which automatically decreased to 12 ml/min in
the second spiral and 7 ml/min in the third sp iral. In the 500 µm spiral, ~95% of particles
>95 µm could be focused whereas in the 200 µm spiral, ~96% of particles >50 µm were
separated. Increasing the number of rec irculations could eventually increase separation
efficiencies even further.
However, it should be taken into account that most industrial bioprocesses are based on cell
cultures with densities several orders of magnitude higher compared to the cell suspensions in
the described experiments. Cell concentrations can easily exceed 10 7 – 10 8 cells/ml. 52 This leads
to a severe increase in cell- to -cell interactions and might subsequently lead to clogging of the
microchannel. In recent work by Maloudi et al. 53 particle separation in scaled-up microchannels
(500/900x2000 µm) was exam ined. By increasing channel size at a constant throughput, cell - to -
cell interactions could be reduced. It was shown that increased channel dimensions clearly lower
the inertia of flow but particle separation was still possible. Additionally it could be shown that

even a single-loop trapezoidal channel is able to separate particles sufficiently. Reducing the
channel’s le ngth could therefore be another possibility to decrease the risk of clogging without
diminishing the device’s performance too heavily. The experiments 53 were conducted using a
suspension of m icrocarriers (100 µm diamete r) and mesenchymal stem c ells. A s shown by
Maloudi et al. in 2018 54 , microcarriers can be separated from mesenchymal stem cells at a fl ow
rate of 30 ml/min and with a total yield of 94% using spiral microchannels. This indicates that an
increased throughput can not only benefit cell separation for industrial purposes but also medical
applications. Different medically relevant cell types like aforesaid mesenchymal stem cells,
circulating tumor cells 19 , 20 and chondrocytes 55 could thereby be separated even faster from cell
mixtures.
IV. CONCLUSIONS
Inertial microfluidic systems could already be applied for several bioprocessing steps ,
including replacement of microfilters, live-dead cell separation, removal of contaminating
microalgae species and separation of specific cells and proteins by linkage to differently sized
beads. As applying inertial systems for bioproce sses was just introduced recently, it is expected
that the presented met hods will be severely improved in the next y ears. The flexibility and
scalability of these systems also allows for application to entirely different processes. The most
relevant issue currently is parallelization, which is essential for upscaling to industry size. As
natural limi ts are set for the flow rate in a mi crochannel, cascading and parallelization are the
only ways to increase throughput beyond 1 l/min while still maintaining efficient particle
separation. Althou gh parallelizing microchannels appears easy, pumps are needed, leading to
high energy costs with incr easing system size which might make these systems unfeasible for
industrial scale p rocesses. Risks of system failure are, however, minimized by a parallel setup.

These risks incl ude primarily clogging of the microchannels through cell agglomeration. Inertial
microfluidic systems still have to be adapted to some biotechnologically relevant organisms like
fungi and bacteria. The bead -linked approach could help here to apply mi crofluidic separation
process es to other protists of different size.
ACKNOWLEDGMENTS
We gratefully acknowledge the funding of the Federal German Ministry of Education and
Science (BMBF) for the program "New products for the bioeconomy", project no. 031B0381
(SepaDiElo). We also acknowledge support by the Open Access Publication Funds of TU Berlin.
REFERENCES
1 D. R. Gossett, W. M. Weaver, A. J. MacH, S. C. Hur, H. T. K. Tse, W. Lee, H. Amini, and D.
Di Carlo, Anal. Bioanal. Chem. 397 , 3249 (2010).
2 A. Dalili, E. Samiei, and M. Hoorfar, Analyst 144 , 87 (2018).
3 D. Stoecklein, and D. Di Carlo, Anal. Chem. 91 , 296 (2019).
4 N. Pamme, Lab Chip 7 , 1644 (2007).
5 C. W. Shields 4th, C. D. Reyes, and G. P. López, Lab Chip 15 , 1230 (2015).
6 A. A. S. Bhagat, H. Bow, H. W. Hou, S. J. Tan, J. Han, and C. T. Lim, Med. Biol. Eng. Comput.
48 , 999 (2010).
7 D. Di Carlo, Lab Chip 9 , 3038 (2009).
8 H. Amini, W. Lee, and D. Di Carlo, Lab Chip 14 , 2739 (2014).
9 J. P. Dijkshoorn, M. A. I. Schutyser, R. M. Wagterveld, C. G. P. H. Schroën, and R. M. Boom,
Sep. Purif. Technol. 173 , 86 (2017).
10 J. Zhang, and D. Yuan, Lab Chip 16 , 10 (2015).
11 Y. Gou, Y. Jia, P. Wang, and C. Sun, Sensors 18 , 1762 (2018).

12 M. S. Syed, M. Rafeie, D. Vandamme, M. Asadnia, R. Henderson, R. A. Taylor, and M. E.
Warkiani, Bioresour. Technol. 252 , 91 (2018).
13 H. Lim, S. M. Back, M. H. Hwang, D. Lee, H. Choi, and J. Nam, Micromachines 10 , (2019).
14 C. Punyadeera, Cancers (Basel). 11 , 1 (2019).
15 G. A. Barabino, J. Biomech. Eng. 140 , 061013 (2018).
16 C. Liu, C. Xue, X. Chen, L. Shan, Y. Tian, and G. Hu, Anal. Bioanal. Chem. 87 , 6041 (2015).
17 B. Miller, M. Jimenez, and H. Bridle, Sci. Rep. 6 , (2016).
18 M. Birkholz, A. Mai, C. Wenger, C. Meliani, and R. Scholz, WIREs Nanomed Nanobiotechnol
8 , 355 (2016).
19 M. E. Warkiani, B. L. Khoo, L. Wu, A. K . P. Tay, A. A. S. Bhagat, J. Han, and C. T. Lim, Nat.
Protoc. 11 , 134 (2015).
20 A. You, M. A. Y. Be, and I. In, Biomicrofluidics 8 , 064117 (2014).
21 A. You, M. A. Y. Be, and I. In, Biomicrofluidics 7 , 054101 (2013).
22 L. Wu, G. Guan, H. W. Hou, A. A. S. Bhagat, and J. Han, Anal. Chem. 84 , 9324 (2012).
23 J. Son, R. Samuel, B. K. Gale, D. T. Carrell, J. M. Hotaling, and J. M. Hotaling,
Biomicrofluidics 11 , 054106 (2017).
24 S. Kim, J. Park, A. Han, and J. Li, Neural Regen. Res. 9 , 1703 (2014).
25 W. C. Lee, A. A. S. Bhagat, S. Huang, K. J. Van Vliet, J. Han, and C. T. Lim, Lab Chip 11 ,
1359 (2011).
26 M. Rafeie, J. Zhang, M. Asadnia, W. Li, and M. E. Warkiani, Lab Chip 16 , 2791 (2016).
27 M. E. Warkiani, A. K. P. Tay, G. Guan, and J. Han, Sci. Rep. 5 , 11018 (2015).
28 T. Roth, L. Sprenger, S. Odenbach, and U. O. Häfeli, Phys. Fluids 30 , 045102 (2018).
29 P. Ho, C. Westerwalbesloh, E. Kaganovitch, A. Grünberger, P. Neubauer, D. Kohleyer, and E.

von Lieres, Microorganisms 7 , 105 (2019).
30 H. S. Kim, A. R. Guzman, H. R. Thapa, T. P. Devarenne, and A. Han, 113 , 1691 (2016).
31 H. S. Kim, T. L. Weiss, H. R. Thapa, T. P. Devarenne, and A. Han, Lab Chip 14 , 1415 (2014).
32 J. M. Martel, and M. Toner, Annu. Rev. Biomed. Eng. 16 , 371 (2014).
33 J. M. Martel, and M. Toner, Phys. Fluids 24 , 032001 (2012).
34 G. Guan, L. Wu, A. A. Bhagat, Z. Li, P. C. Y. Chen, S. Chao, C. J. Ong, and J. Han, Sci. Rep.
3 , 1475 (2013).
35 J. Seo, M. H. Lean, and A. Kole, Appl. Phys. Lett. 91 , 033901 (2007).
36 T. Kwon, H. Prentice, J. De Oliveira, N. Madziva, M. E. Warkiani, J. F. P. Hamel, and J. Han,
Sci. Rep. 7 , 6703 (2017).
37 D. Brindley, K. Moorthy, J. H. Lee, C. Mason, H. W. Kim, and I. Wall, J. Tissue Eng. 2 ,
620247 (2011).
38 T. Klein, N. Heinzel, P. Kroll, M. Brunner, C. Herwig, and L. Neutsch, J. Biotechnol. 207 , 67
(2015).
39 B. C. Batt, R. H. Davis, and D. S. Kompala, Biotechnol. P rog. 6 , 458 (1990).
40 C. A. Freeman, P. S. D. Samuel, and D. S. Kompala, Biotechnol. Prog. 33 , 913 (2017).
41 T. Kwon, R. Yao, J. F. P. Hamel, and J. Han, Lab Chip 18 , 2826 (2018).
42 C. D. Bortner, and J. A. Cidlowski, Cell Death Differ. 9 , 1307 (2002).
43 H. Wang, W. Zhang, L. Chen, J. Wang, and T. Liu, Bioresour. Technol. 128 , 745 (2013).
44 M. Li, H. E. Muñoz, K. Goda, and D. Di Carlo, Sci. Rep. 7 , 1 (2017).
45 M. R. Condina, B. A. Dilmetz, R. Bazaz, J. Meneses, M. Ebrahimi, and P. Hoffmann, Lab Chip
19 , 1961 (2019).
46 D. Di Carlo, D. Irimia, R. G. Tompkins, and M. Toner, Proc. Natl. Acad. Sci. 104 , 18892

(2007).
47 A. Sarkar, H. W. Hou, A. E. Mahan, J. Han, and G. Alter, Sci. Rep. 6 , 23589 (2016).
48 A. J. Mach, and D. di Carlo, Biotechnol. Bioeng. 107 , 302 (2010).
49 G. Reshes, S. Vanounou, I. Fishov, and M. Feingold, Biophys. J. 94 , 251 (2008).
50 M. Robinson, H. Marks, T. Hinsdale, K. Maitland, and G. Coté, Biomicrofluidics 11 , 024109
(2017).
51 A. Abdulla, W. Liu, A. Gholamipour-Shirazi, J. Sun, and X. Ding, Anal. Chem. 90 , 4397
(2 018).
52 F. Tapia, D. Vázquez-ramírez, Y. Genzel, and U. Reichl, Appl. Microbiol. Biotechnol. 100 ,
2121 (2016).
53 R. Moloudi, S. Oh, C. Yang, K. L. Teo, A. Tin, L. Lam, M. Ebrahimi, and M. W. Naing,
Biotechnol. J. 14 , 1800674 (2019).
54 R. Moloudi, S. Oh, C. Yang, K. L. Teo, A. T. Lam, M. E. Warkiani, and M. W. Naing, Sci.
Rep. 8 , 1 (2018).
55 C. A. Tee, Z. Yang, L. Yin, Y. Wu, J. Han, and E. H. Lee, Biomaterials 220 , 119409 (2019).

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