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Detection and enumeration of microbial cells in drinking water using flow cytometry
A new technology for an old problem
Thomas Egli
MICROBES‐IN‐WATER GmbH, Feldmeilen, [email protected]
Keime, Antibiotikaresistez und Desinfektion in WassersystemenBaselArea.swiss und FHNW HLSBasel, 25. Oktober 2016
Outline
Background on hygiene and microbiological safety of drinking waterA bit of historyAn established field: Present concepts and methods
What could be better?Alternative methodsFlow cytometry as an option?
Flow cytometry basicsPrinciple and fluorescent staining optionsTotal cell counting, cell clusters, fingerprintsLive/dead‐staining
Performance in practiceFlow cytometry versus plating
Standardization and validationSLMB: Method 333.1
Some applications in practiceBiostability during storage and distributionAssessing disinfection
The near future: Routine online flow cytometry in drinking water monitoring
Infectious diseases (1)
1800‐1900: Century of worldwide cholera pandemics
1. Pandemic 1817‐23 2. Pandemic 1826‐373. Pandemic 1841‐62 . . . . . . . . . . First international collaboration (Vibrio suspected)4. Pandemic 1864‐755. Pandemic 1882‐96 . . . . . . . . . . Vibrio cholerae shown to be the pathogen6. Pandemic 1899‐237. Pandemic 1936‐… . . . . . . . . . Occasional occurrence (1991 South America)
Source: COX, FEG. The Welcome Trust Illustrated History of Tropical Diseases, 1996
Track of the2nd pandemic
Infectious diseases (2)
Discovery of microorganisms as the cause of infectious diseases
1870‐1900: Revolution in understanding infectious diseases
Louis Pasteur (1822‐1895)
Founders of modern medical microbiology
1854 Cholera (Pacini) 1874 Leprosy 1876 Anthrax 1882 Tuberculosis1884 Typhoid fever 1884 Cholera (Koch) 1884 Diphtheria 1894 Plague
Robert Koch (1843‐1910)Filippo Pacini (1812‐1883)
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~1900: Water hygiene basics
1885 Escherich: Bacillus coli (Escherichia coli) isolated from faeces
1891/2 Péré/Schardinger: Not pathogens directly but E. coli as “indicator”of faecal contamination of water (indicator concept)
1893/4 Koch/Franklands: Number of microbial cells in wateras an indication for its pollution‐‐‐‐> heterotrophic plate count, HPCKoch’s recommendation:< 100/ml CFUs in marine, ground‐ and surface water is safe,this can be reached by slow‐sand filtration
1901 Group of coliforms as an indicator
1904 Improvement: narrowed down to thermotolerant (fecal) coliforms
1977 Further improvement: Genospecies E. coli as best indicator
1
3
2
Safe drinking water
• Ensuring safety and quality of drinking water relies onmonitoring many different parameters:
• PhysicalpH, turbidity, conductivity, temperature, …
• Chemical hardness, organic carbon, pesticides, hormones, …
• Organoleptictaste, smell, …
• Microbiological routinely: total cell number (HPC) and indicator organisms (E. coli, enterococci, P. aeruginosa)when suspected: bacterial pathogens or viruses
Water quality & safety today
… are still used and regulatory base for routine testing of the microbiological quality and safety of (drinking) water all over the world!
In Switzerland: 1899, HPC and coliform methods fixedin the ‘Swiss Food Book’
1883Parameter for assessing the“general” water quality:
HPC(heterotrophic plate count)
# of “all” cultivable microorganisms(< 300 / mL)
1891‐93Hygiene‐relevant parameter:
Escherichia coli
indicating fecal contamination and possible presence of microbial pathogens (0 Ec / 100mL)
Robert Koch, Source Wikipedia 2016
Microbiological methods and concepts developed ~1890
WHO
Methods: Problems
Results only after 1 (Ec) ‐ 10 (HPC) daysHPC: < 1% of microbial cells detectedCHF 40‐60.‐ / testTo date, no continuous monitoring possible; only grab sampling
Both methods are based on cultivation and depend on growth of the target organisms to a visible colony
General quality:Heterotrophic plate count, HPC
Hygiene‐relevant:E. coli, coliforms
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Good sides and limits of E. coli
Escherichia coli has generally been a good indicator for faecal contamination(there is no better, so far!)
However, it is known to fail to indicate absence or disinfection of ‘hardy’ microbes and virusesExample: USA, Milwaukee outbreak 1993, where chlorinated DW was contaminated with sewage, manure (or whatever) with chlorine‐resistant Cryptosporidiumoocysts (up to 0.13 oocysts/L). Estimated 403’000 people affected.
Why still HPC?
Since the 1940s it has been known that HPC allows to detect only ~0.01‐1% of all cells present in a sample (know as “the great plate count anomaly”),… and that those not growing are not ‘dead’(Staley & Konopka, 1985, Am. Rev. Microbiol. 39, 321‐346)
Improvements did not resolve the problem (e.g., R2A and other media). Alternative microscopic methods are too tedious for routine monitoring.
Therefore, in lack of better methods, HPC is still used, e.g., to assess treatment efficiency in water and food industry because E. coli other indicators or pathogens are either absent or present in too low concentrations
Methods: Problems and needs
General quality:Heterotrophic plate count, HPC
Hygiene‐relevant:E. coli, coliforms
Mostly a reliable indicator of faecal contamination… but too slow
For E. coli:much faster method
For HPC: much faster method providing realistic cell # of microbial flora
Very slow and known to detect only a minor, variable fraction (~0.01‐1%) of the microbial flora
…so, let’s start here!
Available methods
Methods used for detecting and counting microbial cells*
Cultivation, plating very slow (standard)
Flow cytometry fast, specific staining, costly
Microscopy (fluorescence) expensive, labour‐intensive
Particle counting fast, unspecific
Biochemical component‐based slow, expensive
Molecular methods (PCR etc.) slow, reliability?
Immunology‐based slow, expensive, specificity?
* Köster W., Egli T., et al. (2003). Analytical Methods for Microbiological Water Quality Testing. In: Assessing Microbial Safety of Drinking Water, pp. 237‐295. OECD, Paris, France, WHO, Geneva, Switzerland.
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The principle
filter 1
(e.g., laser diode 488nm)
flow cell
flow cell(quarz glass
capillary, cuvette)
cells
filter 2
detector 3:fluorescence signal >520 nm
(cell sorting)
waste
fluorescent stain (DNA, surface antibody, etc.)
520nm
detector 2:side scatter 488 nm
detector 1forward scatter 488 nm
sample
light source
filter, condenserLens system
Fluorescent stains for…
DNA
esterases
proteins
red
+
CFDA
H+
‐ ‐+++‐‐
proteins
cellular activitiescellular constituents
RNAs
lipids(pathogen) cell wall
components
fluorescent antibodies
Hoechst, DAPI,
SYBR green
Pyronin Y
RedoxSensorTM
propidium iodide(membrane integrity)
ethidium bromide(H+ efflux pump)
CFDA(esterase activity)
DiBac4(3)(membrane potential)
glucose transport (2‐NBDG)
Nile‐Red
SYPRO
DNA‐stains: what is ‘counted’?
When triggering on green fluorescence signals:
SYBR Green I
Counts all particles that fluoresce green, i.e., contain SYBR Green I bound to ds‐DNA (or ds‐RNA),these are with very high probability more or less ‘intact’ cells
No distinction between:‐ alive, dead, dormant, VBNC, damaged, cultivable, pathogen, good or bad, etc.
Properties of SYBR Green I
SYBR Green I binds to double‐stranded DNA (minor groove) and fluoresces 1000‐times more than in free state
Structure of SYBR Green I
Source: Wikipedia
FL1: 525‐545nm
FL2: >670nm
(or >715nm)
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Data representation
to ‘dot‐plot’from histograms
Data arranged from Kötzsch, Alisch & Egli, 2012, Handbuch: Durchflusszytometrische Analyse von Wasserproben, BAG, Bern
Sample from groundwater at Hardhof, Zurich, after stainingwith SYBR Green I. Total cell concentration: 3.43 x 104 cells/ml.
Often observed: Cell clusters
background
Redflu
orescence intensitiy
signals from
SYBR Green
I
Green fluorescence intensitysignals from SYBR Green I
cells
a single cell’s signal
cell clusters
“Big” and “small” cells
background
Redflu
orescence intensitiy
signals from
SYBR Green
I
Green fluorescence intensitysignals from SYBR Green I
‘HNA’ cells(‘high nucleic acid’‐content)
‘LNA’ cells(‘low nucleic acid’‐content)
big, well‐stained cells
small, badly‐stained cells
TCC and LNA:HNA ratio
background
TCC
Redflu
orescence intensitiy
signals from
SYBR Green
I
Green fluorescence intensitysignals from SYBR Green I
HNA
LNA
Parameters easily obtainedafter SYBR Green I‐staining:
# total cells (TCC )# small cells (LNA)# big cells (HNA)ratio LNA:HNA cells
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Present in most ‘fingerprints’
Drinking water Pond water River water 1 evian
Groundwater Wastewater E. coli River water 2
Helbling et al., SVWG Tagung Zurich, 4 May 2012
Disinfection: ‘Alive’ or ‘dead’ ?
Live and dead E. coli cells stained with LIVE‐or‐DIE™ Viability/Cytotoxicity Kit. Live bacteria exhibit green fluorescence, whereas dead bacteria exhibit red fluorescence.
Some ‘live/dead’ kits for bacteria:
LIVE‐or‐DIETM kit NucView Green/PILIVE/DEAD® BacLightTM kit Syto9/PI
Self‐made assays SYBR Green I/PI(Berney et al. 2008, SLMB 2012)
Concentrations, ratios of the two stains vary
LIVE/DEAD® BacLightTM kit Syto9/PI 0.5 µM/4.5 µMLIVE‐or‐DIETM kit NucView G/PI 3 µM/2 µM Berney et al. (2008) Syto9/PI 5 µM/30 µM for 107 cells/mLBerney et al. (2008) Syto9/PI 0.5 µM/3 µM for 106 cells/mLSLMB (2012) Handbuch SG I/PI 1x/3 µM for 106 cells/mLNescerecka et al. (2014) SG I/PI 1x/6 µM for 106 cells/mL
Microscopic pictures from manufacturer’s brochures
Propidium iodide (PI)
Disinfection: Alive’ or ‘dead’?
Or better:‐ Membrane‐intact / membrane‐damaged cells‐ Intact / permeabilized cells
Typical ‘movement’ of cells during disinfection:
‐ HNA‐cells become first ‘greener’ then ‘red’ (1.)‐ LNA‐cells become simply ‘more red’ (2.)‐ Permeabilized cells then decay and move to background (3.)
From SLMB Handbuch (2012).
Total cell counting (TCC)
Reliable detection limit = 200 cells/mL
Hammes et al. 2008 Water Res. 42:269‐277
R2 = 0.9988
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0 2 4 6 8 10percentage of bottled mineral water (%)
Bacterial con
centratio
n (cells/ml)
(diluted with cell‐free water)
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TCCs in different waters
From: Egli T, Kötzsch S (2015) Flow cytometry for rapid microbiological analysisof drinking water: From science to practice – An unfinished story. In: Flow Cytometryin Microbiology, pp. 175‐2016. Wilkinson M, Ed., Caister Academic Press, Norfolk, UK.
flow cytometrydetection limit
Application: FCM‐TCC vs HPC
distribution system
samples collected
raw water(Lake Zurich)
pre‐ozonation
rapid sandfilter
ozonation granular activated
carbon filter)
slow sandfilter
drinkingwater
reservoir
Drinking water production train and distribution in the town of Zurich
HPC vs. FCM‐TCC vs. ATP
0RW RSF ACF SSF PA N18 N19 N20
HPCs (R2A‐Agar)
200000
400000
600000
800000
1000000
1200000
Total cell cou
nt FCM
(cells/mL)
Total cell count FCMTotal ATP
0
20
40
60
80
100
120
ATP (pM) and
HPC
s (colonies/m
L)
Egli, T, Hammes F (2010). Neue Methoden für die Wasseranalytik. gwa Gas, Wasser & Abwasser 4, 315‐324.
HPC ≠ FCM‐TCC
0–200
vs
10‘000–1‘000‘000
Unpublished data from the Waterworks of Zurich and Eawag from the treatment and distribution system
0
20
40
60
80
100
120
140
160
180
200
0.0E+00 2.0E+05 4.0E+05 6.0E+05 8.0E+05 1.0E+06 1.2E+06 1.4E+06
FCM total cell count (cells/mL)Heterotroph
ic plate cou
nt (colon
ies/mL)
Public fountains
Ends of atown quarter
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No correlation
Flow cytometry total cell countno legally established valuebut ~20’000‐200’000/mLin safe drinking water
Heterotrophic plate count (HPC)< 2‐300/mL for drinking water
(legally established in some countries)
HPC and FCM‐TCC measure ‘two different things’
FCM‐TCC becoming ‘official’
Method development atEawag since ~2004
Testing the methodat WVZ ~2005
FCM available atWVZ since ~2007 anduse of methods in practice
Water works Basel andAmsterdam, CantonalLab ZH follow ~2009/2011EU project: Successful demonstrationsin DE, NL, PT, NO, DK, LV, NA
2011‐12: Standardisation andValidation of TCC method together with SVGW, BAG, Cantonal Labs,WVZ and Basel, private firms;Goal: acceptance of method
Results from validation
Total cell count (14 persons)
LNA/HNA (14 persons)
groundwater spring water lake water treated HI after flushing
BIG (HNA)cells
Small (LNA) cells
R‐STDV = 8.08 %
R‐STDV = 6.92 %
R‐STDV = 7.97 %
R‐STDV = 4.56 %
R‐STDV = 6.75 %
# of data sets
groundwater
beads
spring water
lake water treated
house installationafter flushing
Standardization, validationDecember 19, 2012:Fed. Office of Public Health (BAG) has included the method in the Swiss Food Regulations(Schweizerisches Lebensmittelbuch) including a guideline for interpretation of results.
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How to use FCM‐TCC data?
Comments on interpretation of HPC data from the WHO‘[HPC] has little value as an index of pathogen presence but can be useful in operational monitoring as a treatment and disinfectant indicator, where the objective is to keep numbers as low as possible.’
‘…the current EU Directive [and WHO] do not set numerical standards of guideline values for colony counts, which are defined as indicator parameters, but state that there should be no abnormal change.’
Exactly the same applies for FCM‐TCC, just that the numerical values are in the range of ten‐ or hundred thousand cells, instead of a few hundred colonies.[Comment: Some countries still cling totheir tolerance levels, e.g., Switzerland]
World Health Organization (WHO) (2011). Guidelines fordrinking‐water quality – 4th ed. WHO, Geneva, Switzerland.
Bartram, J. et al., eds. (2003). Heterotrophic plate counts and drinking‐water safety. The significance of HPCs for water quality and human health. IWA Publishing, London UK.
Biostability during distribution
Drinking water produced fromlake water at SeewasserwerkLengg, Zurich
Residence time: 6‐50 hSampling Nov.‐March 2008/2009
Lautenschlager et al. (2013) Water Res. 47, 3015‐3025
distribution system
1 Reservoir Lengg
2
4
5
Reservoir Witikon
Reservoir Sonnenberg
ReservoirLooren
ReservoirOrelli
3
Stoicheiometry of growth
1 µg
of assimilable organic carbon (AOC)supports growth of
≈107 microbial cells !
Hammes & Egli (2005) Environ Sci Technol 39, 3289‐3294
Cell concentration is stable
0
200000
400000
600000
800000
1000000
1200000
RW RSF ACF SSF PA N18 N19 N20
0
20
40
60
80
100
120
ATP (pM) and
HPC
s (colon
ies/mL)
Total cell count FCM
Total cell cou
nt FCM
(cells/mL)
Biostable water !!!
Lautenschlager et al. (2013) Water Res. 47, 3015‐3025
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…and stable flora composition
Each line corresponds to one “species”
Pearson correlation [0.0%-100.0%]DGGE
100
500-50
DGGE
4 network March
2 network March
3 network March
5 network March
3 network Nov.
4 network Nov.
5 network Nov.
5 network Dec.
3 network Dec.
2 network Dec.
4 network Dec.
1 network Jan
1 network Dec.
2 network Nov.
1 network Nov.
4 network Feb
2 network Feb
1 network Feb
1 network March
3 network Feb
4 network Jan
2 network Jan
5 network Jan
7 network Jan
7 network Dec.
5 network Feb
3 network Jan
6 network March
6 network Feb
7 network Feb
1 Reservoir Lengg
2,3,4,5 Net
Not only the cell number but also the composition of the microbial flora is stable during distribution !!!
“Finger print” of the microbial flora in drinking water using DGGE(DGGE = Denaturing gradient gel electrophoresis of amplified pieces of rRNA genes)
Each line corresponds to one “species”
Regrowth during distribution
Full scale drinking water system at Riga (Latvia)
Water is chlorinated before distribution,but massive regrowth takes place in distribution system(indicated by FCM‐TCC but not HPC),and even occurrence ofE. coli during summer.
FCM‐TCC
HPC
(CFU
/mL)
FCM TCC
(cells/mL)
HPC
Nescerecka et al. (2014) Biological instability in a chlorinated drinking water distribution network. PLOS ONE 9(5), e96354.
Regrowth during distribution
…and the corresponding ‘live/dead’dot‐plots for increasing residence times
Nescerecka et al. (2014) Biological instability in a chlorinated drinking water distribution network. PLOS ONE 9(5), e96354.
(In)Stability of groundwaters
(Egli & Kötzsch (2015). Flow Cytometry for Rapid Microbiological Analysis ofDrinking‐Water. From Science to Practice – An Unfinished Story. In: FlowCytometry in Microbiology, Wilkinson ed., Caister Academic Press, Norfolk UK.
0
20
40
60
80
100 TCC
ICC
PCC
LNA HNA
TOC
DOC
0
20
40
60
80
100 TCC
ICC
PCC
LNA HNA
TOC
DOC MÄR 2010
MAI 2010
JUN 2010
JUL 2010
SEP 2010
Month 1
Month 2
Month 3
Month 4Month 5
Groundwater A in [%] Groundwater B in [%]
TCC: Total cell countICC: Intact cell countPCC: Permeabilized cell countLNA: Low nucleic acid content cellsHNA: High nucleic acid content cellsTOC: Total organic carbonDOC: Dissolved organic carbon
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Disinfection: DW flora
From: Ramseier et al. (2011) Water Res. 45: 1490‐1500
Flow cytometric assessment of total cell count and of membrane‐intact/damaged cells in drinking water as a function of exposure to ClO2. Initial concentrations of ClO2 were: 0.75 mg/L (a,b,c,e,f,g) and 2.5 mg/L (d,h).
SGI only SGI only SGI onlySGI only
SGI+PI
SGI+PI
SGI+PI
SGI+PI
ClO2
Increasing cxt
12
3
‘Live/dead’ and free chlorine
From: Gillespie et al. (2014) Water Res. 65: 224‐234
Effect of free chlorine concentration on the proportion of intact cells in a water treatment and its supplied distribution system.
The dotted line indicates a critical free chlorine concentration threshold for this system.
most cells dead
most cells alive
Online flow cytometry
First steps inonline flow cytometry
TCC in practice
All manual work, with all commercially available cytometers
Sample
Mix,5 sec Incubate for 13 min
(or longer) at 37±2 °C in the dark
1000 µL
10 µL
Use directly
…or store at 4 °C in the dark for later use
SYBR Green Idiluted in DMSO
Analyze data with an FCM software‐‐> result, decision
Discrete sampling, transport to the lab, staining and FCM‐analysis
Sampling &transport
Sampling, fixation, transport & storage
Treattoxic waste
Prepare sheath fluid, cleaning liquid, stain,
standard beads
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Online flow cytometry
Hammes et al. (2012) Cytometry Part A, 81A:506‐512.
Online flow cytometry
Background:non‐chlorinatedtap water
Hammes et al. (2012) Cytometry Part A, 81A:506‐512.
Detecting changes in cell concentration
Online flow cytometry
Hammes et al. (2012) Cytometry Part A, 81A:506‐512.
Total bacterial cell concentration in AC‐biofilter effluent as a function of the flow rate measured with on‐line FCM during 24 hours (SYBR Green I staining).
In practice: TCC in AC‐biofilter effluent at different flow rates
Online flow cytometry
Hammes et al. (2012) Cytometry Part A, 81A:506‐512.
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On‐line FCM ‘BactoSense’
Market launch end 2016100% validated and protected by 3 patents
TCC and LNA/HNA in 15 minFully automated for online usageFor the first time: microbiological analysisof drinking water before distributionOption to set threshold alarmOnline or manual loading optionCHF < 3.‐/test (cartridge for 1000 tests)For industrial/field use
… and TCC with BactoSense
Sample
Mix,5 sec Incubate for 13 min
(or longer) at 37±2 °C in the dark
1000 µL
10 µL
Use directly
…or store at 4 °C in the dark for later use
SYBR Green Idiluted in DMSO
Analyze data with an FCM software‐‐> result, decision
Sampling &transport
Sampling, fixation, transport & storage
Treattoxic waste
Sends the data to your lab or phone
… or can initiate action when in alarm mode
BactoSense does all this automatically on‐site, e.g. every 20 min
Prepare sheath fluid, cleaning liquid, stain,
standard beads
Discrete sampling, transport to the lab, staining and FCM‐analysis
Take‐home messagesThe two established methods for microbiological drinking water analysis are based on cultivation on nutrient agar plates; these methods are slow: E. coli counting requires at least 1, HPC up to 10 days to obtain the result.
E. coli test gives realistic results but the HPC‐results have been known for a long time to be much too low.
Flow cytometry‐based cell counting allows to easily, quickly (< 15 minutes) and quantitatively detect microbial cells after staining with a DNA‐binding fluorescent dye.
FCM total cell counting has been developed in Switzerland over the last 15 years and tested in practice in several Swiss water works. It has proven useful for quickly monitoring general microbiological quality in drinking water production processes and distribution networks.
FCM total cell counting was standardized and validated and is officially accepted by the Federal Office of Public Health (Swiss Food Book, method 333.1).
FCM allows to determine many other properties of individual cells quickly (e.g., live/dead‐staining, enzymatic cellular activities)
Shortly, small cytometers for online total cell counting will be commercially available.
Thanks
…for your attention
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Thanks
Labor SpiezABC-Schutz
Bundesamt für Gesundheit BAG
Bundesamt für Umwelt BAFU
to all my students, collaboratorsand financial supporters….
...and
Selected papersBerney M, Hammes F, Bosshard F, Weilenmann HU, Egli T (2007).
Assessment and interpretation of bacterial viability by using the LIVE/DEAD BacLight Kit in combination with flow cytometry. Appl. Environ. Microbiol. 73, 3283‐3290.
Berney M, Vital M, Hülshoff I, Weilenmann HU, Egli T, Hammes F (2008). Rapid, cultivation‐independent assessment of microbial viability in drinking water. Water Res. 42, 2010‐4018.
Besmer MD, Weissbrodt DG, Kratochvil BE, Sigrist JA, Weyland MS, Hammes F (2014). The feasibility of automated online flow cytometry for in‐situ monitoring of microbial dynamics in aquatic ecosystems. Front. Microbiol. 5, article 265.
Egli T (2012). Mikrobiologische Trinkwasseranalyse. Entwicklung, Stand, Ausblick. Aqua & Gas 5, 14‐22.
Egli T, Bucheli M (2014). Wie viele Zellen sind im Trinkwasser? Durchflusszytometrie in der mikrobiologische Trinkwasseranalyse: wie weiter? Aqua & Gas 11, 90‐98.
Egli, T, Hammes F (2010). Neue Methoden für die Wasseranalytik. gwaGas, Wasser & Abwasser 4, 315‐324.
Egli T, Kötzsch S (2015). Flow cytometry for rapid microbiological analysis of drinking water: From science to practice – An unfinished story. In: Flow Cytometry in Microbiology, pp. 175‐2016. Wilkinson M, Ed., Caister Academic Press, Norfolk, UK.
Gillespie S, Lipphaus P, Green J, Parsons S, Weir P, Juskowiak K, Jefferson B, Jarvis P, Nocker A (2014). Assessing microbiological water quality in drinking water distribution systems with disinfectant residual using flow cytometry. Water Res. 65, 224‐234.
Hammes F, Berney M, Wang Y, Vital M, Köster O, Egli T (2008). Flow‐cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 42, 269‐277.
Hammes F, Broger T, Weilenman HU, Vital M, Helbing J, Bosshart U, Huber P, Odermatt RP, Sonnleitner B (2012). Development and laboratory‐scale testing of a fully automated online flow cytometer for drinking water analysis. Cytometry Part A 81A, 508‐516.
Keserue HA, Baumgartner A., Felleisen R, Egli T (2012). Rapid detection of total and viable Legionella pneumophila in tap water by immunomagnetic separation, double fluorescent staining and flow cytometry. Microb. Biotechnol. 5, 753‐763.
Lautenschlager K, Boon N, Wang Y, Egli T, Hammes F (2010). Overnight stagnation of drinking water in household taps induces microbial growth and changes in community composition. Water Res. 44, 4868‐4877.
Nescerecka A, Rublis J, Vital M, Juhna T, Hammes F (2014). Biological instability in a chlorinated drinking water distribution network. PLOSone 9, e96354.
Prest EI, Hammes F, Kötzsch S, van Loosdrecht MCM, Vrouwenvelder JS (2013). Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method. Water Res. 47, 7131‐7142.
Ramseier MK, von Gunten U, Freihofer P, Hammes F (2011). Kinetics of membrane damage to high (HNA) and low (LNA) nucleic acid bacterial clusters in drinking water by ozone, chlorine, chlorine dioxide, monochloramine, ferrate(VI), and permanganate. Water Res. 45, 1490‐1500.
SLMB (2012). Methode 333.1. Bestimmung der Totalzahl und des quantitativen Verhältnisses der Zellen niedrigen bzw. hohen Nukleinsäuregehalts in Süsswasser mittels Durchflusszytometrie. Schweizerisches Lebensmittelbuch. Schweizerisches Bundesamt für Gesundheit (BAG), Bern.
SLMB (2012). Method 333.1. Determining the total cell count and ratios of high and low nucleic acid content cells in freshwater using flow cytometry. Federal Office of Public Health, Bern, Switzerland.
SLMB (2012). Durchflusszytometrische Analyse von Wasserproben. Handbuch zur Methode 333.1, zusammengestellt von S. Kötzsch, S. Alisch und T. Egli. Schweizerisches Bundesamt für Gesundheit (BAG).